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Malik, Prasanta, and Samiran Das. "AI-statistical limit points and AI-statistical cluster points." Filomat 36, no. 5 (2022): 1573–85. http://dx.doi.org/10.2298/fil2205573m.
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In this paper using a non-negative regular summability matrix A and a non trivial admissible ideal I of subsets of N we have introduced the notion of AI-statistical limit point as a generalization of A-statistical limit point of sequences of real numbers. We have also studied some basic properties of the sets of all AI-statistical limit points and AI-statistical cluster points of real sequences including their interrelationship. Also introducing additive property of AI-density zero sets we have established AI-statistical analogue of some completeness theorems of R.
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Benjumeda Wynhoven, Isabel María, and Claudio Córdova Lepe. "Analysis of IPV success treatment from an AI approach." PLOS One 20, no. 6 (2025): e0323945. https://doi.org/10.1371/journal.pone.0323945.
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Intimate partner violence (IPV) is a serious social problem in Chile. Understanding the patterns of internalization and the motivations maintaining it is crucial to design optimal treatments that ensure adherence and completeness. This, in addition, is essential to prevent revictimization and improve the quality of life of both victims and their children.The present study analyzes the success of a psychological treatment offered by a Chilean foundation helping IPV victims. A database analysis containing 1,279 cases was performed applying classical statistics and artificial intelligence methods. The aim of the research was to search for cluster grouping and to create a classification model that is able to predict IPV treatment completeness. The main results demonstrate the presence of two main clusters, one including victims who completed the treatment (cluster 1) and a second one containing victims who did not complete the treatment (cluster 2). Cluster classification using an XGBoost model of the treatment completeness had an accuracy of 81%. The results showed that living with the aggressor, age and educational level had the greatest impact on the classification. Considering these factors as input variables allow for a higher precision on the treatment completeness prediction. To our knowledge, this is the first study performed in Chile that uses AI for cluster grouping and for analyzing the variables contributing to the success of an IPV victims’ treatment.
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Govender, Reginald Gerald. "My AI students: Evaluating the proficiency of three AI chatbots in <i>completeness</i> and <i>accuracy</i>." Contemporary Educational Technology 16, no. 2 (2024): ep509. http://dx.doi.org/10.30935/cedtech/14564.
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A new era of artificial intelligence (AI) has begun, which can radically alter how humans interact with and profit from technology. The confluence of chat interfaces with large language models lets humans write a natural language inquiry and receive a natural language response from a machine. This experimental design study tests the capabilities of three popular AI chatbot services referred to as my AI students: Microsoft Bing, Google Bard, and OpenAI ChatGPT on <i>completeness</i> and <i>accuracy</i>. A Likert scale was used to rate c<i>ompleteness </i>and <i>accuracy,</i> respectively, a three-point and five-point. Descriptive statistics and non-parametric tests were used to compare marks and scale ratings. The results show that AI chatbots were awarded a score of 80.0% overall. However, they struggled with answering questions from the higher Bloom’s taxonomic levels. The median <i>completeness</i> was 3.00 with a mean of 2.75 and the median <i>accuracy</i> was 5.00 with a mean of 4.48 across all Bloom’s taxonomy questions (n=128). Overall, the<i> completeness</i> of the solution was rated mostly incomplete due to limited response (76.2%), while <i>accuracy</i> was rated mostly correct (83.3%). In some cases, generative text was found to be verbose and disembodied, lacking perspective and coherency. Microsoft Bing ranked first among the three AI text generative tools in providing correct answers (92.0%). The Kruskal-Wallis test revealed a significant difference in <i>completeness </i>(asymp. sig.=0.037, p&lt;0.05) and <i>accuracy</i> (asymp. sig.=0.006, p&lt;0.05) among the three AI chatbots. A series of Mann and Whitney tests were carried out showing no significance between AI chatbots for <i>completeness</i> (all p-values&gt;0.015 and 0&lt;r&lt;0.2), while a significant difference was found for <i>accuracy</i> between Google Bard and Microsoft Bing (asymp. sig.=0.002, p&lt;0.05, r=0.3 medium effect). The findings suggest that while AI chatbots can generate comprehensive and correct responses, they may have limits when dealing with more complicated cognitive tasks.
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Zavalin, Vyacheslav, and Oksana L. Zavalina. "Are we there yet? Evaluation of AI-generated metadata for online information resources." Information Research an international electronic journal 30, iConf (2025): 732–40. https://doi.org/10.47989/ir30iconf47215.
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Introduction. Generative AI tools are increasingly used in creating descriptive metadata the quality of which is key for information discovery and support of information user tasks. Machine-readable online information resources such as websites naturally lend themselves to automatic metadata creation. Yet, assessments of AI-generated metadata for them are lacking. AI metadata quality research to date is limited to 2 metadata standards. Method. This experimental study assessed the quality of AI-generated descriptive metadata in 4 most widely used standards: Dublin core, MODS, MARC, and BIBFRAME. Three generative AI tools – Gemini, Gemini advanced, and ChatGPT4 – were used to create metadata for an educational website. Analysis. Zero-shot queries prompting AI tools to generate metadata followed the same structure and included the link to metadata scheme’s openly accessible documentation. Comparative in-depth analysis of accuracy and completeness of entire resulting AI-generated metadata records was performed. Results. Overall, AI-generated metadata does not meet the quality threshold. ChatGPT performs somewhat better than 2 other tools on completeness, but accuracy is similarly low in all 3 tools. Conclusions. Current metadata-generating effectiveness of AI tools does not allow to conclude that involvement of human metadata experts in creation of quality (and therefore functional) metadata can be significantly reduced without strong negative impact on information discovery.
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Lee, Sena, and Jung-Won Lee. "The Impact of AI Travel Planner Tourism Information Quality on Continuance Intention: Focusing on Generation Z College Students." Convergence Tourism Contents Society 10, no. 3 (2024): 137–52. https://doi.org/10.22556/jctc.2024.10.3.137.
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Purpose: This study aimed to examine the structural relationship among AI travel planner tourism information quality, perceived usefulness, satisfaction, and continuance intention by applying the modified Post Acceptance Model(PAM) centered on Generation Z. Methods: This study conducted a survey of Generation Z college students who have experience using AI travel planners in travel apps. The survey period was approximately two weeks from November 2024, and a convenience sampling method was used to secure a sample of 152 people and conduct an analysis. Results: The empirical analysis results of this study are as follows. First, the AI travel planner tourism information quality factors were derived as completeness and appropriateness. Second, the AI travel planner tourism information quality factors completeness and appropriateness were found to affect perceived usefulness. Third, the perceived usefulness of the AI travel planner was found to affect satisfaction. Fourth, satisfaction was found to affect continuance intention. These results highlight the importance of providing high-quality, reliable, personalized information through AI travel planners to improve user engagement, satisfaction, and retention. Conclusion: The significance of this study lies in the fact that it systematically verified the travel plan derivation process of AI travel planner users by confirming the influence of AI travel planner tourism information quality on perceived usefulness, satisfaction, and continuance intention.
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Abidin, Zain. "Evaluating the Precision and Dependability of Medical Answers Generated by ChatGPT." Journal of Science, Technology, Education, Art and Medicine 1, no. 1 (2024): 13. https://doi.org/10.63137/jsteam.744858.
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Objective This study aims to assess the accuracy and depth of ChatGPT’s responses to medical questions posed by physicians, providing preliminary evidence of its reliability in offering precise and comprehensive information. Furthermore, the study will shed light on the limitations inherent in AI-generated medical advice. Methods This research involved 10 physicians formulating questions for ChatGPT without patient-specific data. Approximately 29% of the 35 invited doctors participated, creating eight questions each. The questions covered easy, medium, and hard levels, with yes/no or descriptive responses. ChatGPT’s responses were evaluated by physicians for accuracy and completeness using established Likert scales. An internal validation re-submitted questions with low accuracy scores, and statistical measures analyzed the outcomes, revealing insights into response consistency and variation over time. Results The analysis of 80 ChatGPT-generated answers revealed a median accuracy score of 4 (mean 4.7, SD 2.6) and a median completeness score of 2 (mean 1.8, SD 1.5). Notably, 30% of responses achieved the highest accuracy score (6), and 38.7% were rated nearly all correct (5), while 8% were deemed completely incorrect (1). Inaccurate answers were more common for physician-rated hard questions. Completeness varied, with 45% considered comprehensive, 37.5% adequate, and 17.5% incomplete. Modest correlation (Spearman’s r = 0.3) existed between accuracy and completeness across all questions. Conclusion Integrating language models like ChatGPT in medical practice shows promise, but cautious considerations are crucial for safe use. While AI-generated responses display commendable accuracy and completeness, ongoing refinement is needed for reliability. This research lays a foundation for AI integration in healthcare, underscoring the importance of continuous evaluation and regulatory measures to ensure safe and effective implementation.
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Bourré, Natalie. "Can readers spot the AI impostor in healthcare writing?" Medical Writing 32, no. 3 (2023): 38–40. http://dx.doi.org/10.56012/fwhk6920.
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The use of artificial intelligence (AI) writing assistants in the healthcare industry is becoming increasingly prevalent. These tools can help medical writers to generate content more quickly and efficiently, but they also raise concerns about the accuracy and completeness of the information that is produced. This study investigated whether readers can distinguish between health-related texts written by humans and those generated by AI writing assistants. A survey of 164 respondents found that slightly more than half could correctly identify the source of the healthcare text. Differences between healthcare professionals and non-healthcare professionals were not statistically significant. Medical writers were better at recognising that a text had been written by an AI model than were non-medical writers (P<.05). These findings suggest that it is important for organisations to establish clear guidelines regarding the use of AI writing assistants in healthcare. The authors of health-related content should be required to identify whether their work has been completed by a human or an AI writer, and organisations should develop processes for evaluating the accuracy and completeness of AI-generated content. This study has several limitations, including the small sample size. However, the findings provide valuable insights into the need for organisations to develop clear guidelines for their use.
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Ponzo, Valentina, Rosalba Rosato, Maria Carmine Scigliano, et al. "Comparison of the Accuracy, Completeness, Reproducibility, and Consistency of Different AI Chatbots in Providing Nutritional Advice: An Exploratory Study." Journal of Clinical Medicine 13, no. 24 (2024): 7810. https://doi.org/10.3390/jcm13247810.
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Background: The use of artificial intelligence (AI) chatbots for obtaining healthcare advice is greatly increased in the general population. This study assessed the performance of general-purpose AI chatbots in giving nutritional advice for patients with obesity with or without multiple comorbidities. Methods: The case of a 35-year-old male with obesity without comorbidities (Case 1), and the case of a 65-year-old female with obesity, type 2 diabetes mellitus, sarcopenia, and chronic kidney disease (Case 2) were submitted to 10 different AI chatbots on three consecutive days. Accuracy (the ability to provide advice aligned with guidelines), completeness, and reproducibility (replicability of the information over the three days) of the chatbots’ responses were evaluated by three registered dietitians. Nutritional consistency was evaluated by comparing the nutrient content provided by the chatbots with values calculated by dietitians. Results: Case 1: ChatGPT 3.5 demonstrated the highest accuracy rate (67.2%) and Copilot the lowest (21.1%). ChatGPT 3.5 and ChatGPT 4.0 achieved the highest completeness (both 87.3%), whereas Gemini and Copilot recorded the lowest scores (55.6%, 42.9%, respectively). Reproducibility was highest for Chatsonic (86.1%) and lowest for ChatGPT 4.0 (50%) and ChatGPT 3.5 (52.8%). Case 2: Overall accuracy was low, with no chatbot achieving 50% accuracy. Completeness was highest for ChatGPT 4.0 and Claude (both 77.8%), and lowest for Copilot (23.3%). ChatGPT 4.0 and Pi Ai showed the lowest reproducibility. Major inconsistencies regarded the amount of protein recommended by most chatbots, which suggested simultaneously to both reduce and increase protein intake. Conclusions: General-purpose AI chatbots exhibited limited accuracy, reproducibility, and consistency in giving dietary advice in complex clinical scenarios and cannot replace the work of an expert dietitian.
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K, Sabitha. "AI-Powered Cybercrime Reporting System." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 3596–601. https://doi.org/10.22214/ijraset.2025.70702.
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Abstract:With the exponential increase in digital threats, the traditional cybercrime reporting process remains largely unstructured and inaccessible to common users.This article proposes an AI-powered cybercrime reporting system that takes advantage of natural language processing and machine learning to offer an intelligent, guided interface for victims to report incidents.The system employs a fine-tuned RoBERTa-base model to classify cybercrimes into 23 predefined categories based on user descriptions and dynamically adjusts the reporting flow to collect appropriate data. Additionally, it enables secure digital evidence handling and automated PDF report generation for law enforcement. The proposed system improves reporting accuracy, user confidence, and evidence completeness, representing a transformative change in digital law enforcement support tools
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Burnette, Hannah, Aliyah Pabani, Mitchell S. von Itzstein, et al. "Use of artificial intelligence chatbots in clinical management of immune-related adverse events." Journal for ImmunoTherapy of Cancer 12, no. 5 (2024): e008599. http://dx.doi.org/10.1136/jitc-2023-008599.
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BackgroundArtificial intelligence (AI) chatbots have become a major source of general and medical information, though their accuracy and completeness are still being assessed. Their utility to answer questions surrounding immune-related adverse events (irAEs), common and potentially dangerous toxicities from cancer immunotherapy, are not well defined.MethodsWe developed 50 distinct questions with answers in available guidelines surrounding 10 irAE categories and queried two AI chatbots (ChatGPT and Bard), along with an additional 20 patient-specific scenarios. Experts in irAE management scored answers for accuracy and completion using a Likert scale ranging from 1 (least accurate/complete) to 4 (most accurate/complete). Answers across categories and across engines were compared.ResultsOverall, both engines scored highly for accuracy (mean scores for ChatGPT and Bard were 3.87 vs 3.5, p<0.01) and completeness (3.83 vs 3.46, p<0.01). Scores of 1–2 (completely or mostly inaccurate or incomplete) were particularly rare for ChatGPT (6/800 answer-ratings, 0.75%). Of the 50 questions, all eight physician raters gave ChatGPT a rating of 4 (fully accurate or complete) for 22 questions (for accuracy) and 16 questions (for completeness). In the 20 patient scenarios, the average accuracy score was 3.725 (median 4) and the average completeness was 3.61 (median 4).ConclusionsAI chatbots provided largely accurate and complete information regarding irAEs, and wildly inaccurate information (“hallucinations”) was uncommon. However, until accuracy and completeness increases further, appropriate guidelines remain the gold standard to follow
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Sembiring, Mikael, and Fahrul Novagusda. "Enhancing Data Security Resilience in AI-Driven Digital Transformation: Exploring Industry Challenges and Solutions Through ALCOA+ Principles." Acta Informatica Medica 32, no. 1 (2024): 65. http://dx.doi.org/10.5455/aim.2024.32.65-70.
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data integrity as the maintenance of accuracy, consistency, and completeness of data over time.Recently, “artificial intelligence” has become prevalent across industries, education, culture,and technology, denoting systems that mimic human intelligence and critical thinking using computers and related technologies. Objective: This article examines the construction of a robust artificial intelligence (AI) system and the incorporation of ALCOA+ principles for data validation, with a specific focus on enhancing data certainty and security. Methods: This study was carried out through a comprehensive review of various Scopus-indexed literature over the past decade. Results and Discussion: AI has been widely applied in Manufacturing System Optimization, involving organizing production systems, including machines, robots, conveyors, and related operations like maintenance and material handling. Moreover, it’s used for Process Monitoring, Diagnostics, and Prognostics in medicine, as well as supervision and regulation in industries. Yet, it’s not immune to shortcomings, which could result in system biases and jeopardize data security. Conclusion: This article explores the creation of a robust AI system, implementing ALCOA+ for data validation in AI-Driven Digital Transformation to improve data certainty and security in industries. It involves systematically recording AI system activities, ensuring database validity, sustaining data recording practices, regularly updating records, ensuring authenticity and completeness, and facilitating data accessibility for review and audits. As AI integration in education advances, there’s a crucial need for oversight to maintain data integrity in these systems.
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Subramanian, Brughanya, Ramachandran Rajalakshmi, Sobha Sivaprasad, Chetan Rao, and Rajiv Raman. "Assessing the appropriateness and completeness of ChatGPT-4’s AI-generated responses for queries related to diabetic retinopathy." Indian Journal of Ophthalmology 72, Suppl 4 (2024): S684—S687. http://dx.doi.org/10.4103/ijo.ijo_2510_23.
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Objective: To evaluate the appropriateness of responses generated by an online chat-based artificial intelligence (AI) model for diabetic retinopathy (DR) related questions. Design: Cross-sectional study. Methods: A set of 20 questions framed from the patient’s perspective addressing DR-related queries, such as the definition of disease, symptoms, prevention methods, treatment options, diagnostic methods, visual impact, and complications, were formulated for input into ChatGPT-4. Peer-reviewed, literature-based answers were collected from popular search engines for the selected questions and three retinal experts reviewed the responses. An inter-human agreement was analyzed for consensus expert responses and also between experts. The answers generated by the AI model were compared with those provided by the experts. The experts rated the response generated by ChatGPT-4 on a scale of 0–5 for appropriateness and completeness. Results: The answers provided by ChatGPT-4 were appropriate and complete for most of the DR-related questions. The response to questions on the adverse effects of laser photocoagulation therapy and compliance to treatment was not perfectly complete. The average rating given by the three retina expert evaluators was 4.84 for appropriateness and 4.38 for completeness of answers provided by the AI model. This corresponds to an overall 96.8% agreement among the experts for appropriateness and 87.6% for completeness regarding AI-generated answers. Conclusion: ChatGPT-4 exhibits a high level of accuracy in generating appropriate responses for a range of questions in DR. However, there is a need to improvise the model to generate complete answers for certain DR-related topics.
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Cruz Rivera, Samantha, Xiaoxuan Liu, An-Wen Chan, Alastair K. Denniston, and Melanie J. Calvert. "Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension." Nature Medicine 26, no. 9 (2020): 1351–63. http://dx.doi.org/10.1038/s41591-020-1037-7.
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AbstractThe SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
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Tri Syamsi Julianto and Stelie Ratumanan. "Pemanfaatan Generatif AI dalam Pembelajaran Bahasa untuk Siswa SD: Pendekatan Inovatif dalam Meningkatkan Kemampuan Menulis." Bima Journal of Elementary Education 1, no. 2 (2023): 48–52. http://dx.doi.org/10.37630/bijee.v1i2.1224.
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This research explores the importance of utilizing Generative Artificial Intelligence (AI) in language learning for Elementary School (SD) students. The research aims to identify whether the use of Generative AI can enhance the writing skills of elementary school students. The study was conducted with elementary school students from two different schools as the research population located in the Nanga Pinoh District, Melawi Regency. The research sample consists of two classes that used Generative AI in language education, while the other two classes served as the control group. Data analysis techniques included evaluating the students' written work, considering completeness, structure, and creativity. The results of the study show a significant improvement in the writing skills of students who received education with the utilization of Generative AI, demonstrating a substantial enhancement in the completeness, structure, and creativity of their writing compared to the control group. In conclusion, the use of Generative AI in language education for primary school students represents an innovative approach that can significantly enhance their writing skills. The findings of this research provide concrete evidence that Generative AI technology holds great potential for enriching the language learning experience and writing skills of primary school students. Suggestions for further research include the development of more specific and contextual teaching strategies, as well as expanding the range of subjects that can benefit from Generative AI. In this rapidly evolving technological era, further research on the use of AI in primary education will contribute to creating a more innovative learning environment that supports the development of students' language skills.
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Farrugia, Y., M. Sciberras, H. Gordon, et al. "P666 Accuracy of information given by ChatGPT for patients with Inflammatory Bowel Disease in relation to ECCO guidelines." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i1267—i1268. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0796.
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Abstract Background As acceptance of AI platforms increases, more patients will consider these tools as sources of information. The ChatGPT architecture utilizes a neural network to process natural language, thus generating responses based on the context of input text. The accuracy and completeness of ChatGPT3.5 in the context of Inflammatory Bowel Disease (IBD) remains unclear. Methods In this prospective study, 38 questions worded by IBD patients were inputted into ChatGPT3.5. The following topics were covered: 1) Crohn’s Disease, Ulcerative Colitis, and malignancy, 2) maternal medicine 3) infection and vaccination 4) complementary medicine. Responses given by ChatGPT were assessed for accuracy (1 – completely incorrect to 5 – completely correct) and completeness (3-point Likert scale; range 1 – incomplete to 3 – complete) by 14 expert gastroenterologists, in comparison with relevant ECCO guidelines. Results In terms of accuracy, most replies (84.2%) had a median score of ≥4 (IQR:2) and a mean score of 3.87 (SD: +/- 0.6). For completeness, 34.2% of the replies had a median score of 3 and 55.3 % had a median score of between 2 and &lt;3. Overall, the mean rating was 2.24 (SD: +/- 0.4, Median:2 IQR :1). Though group 3 and 4 had a higher mean for both accuracy and completeness, there was no significant scoring variation between the 4 question groups (Kruskal-Wallis test p:&gt;0.05) (Table 1). However, statistical analysis for the different individual questions revealed a significant difference both for accuracy (p&lt;0.001) and completeness (p&lt;0.001). The questions which rated the highest for both accuracy and completeness were related to smoking, while the lowest rating was related to screening for malignancy and vaccinations especially in the context of immunosuppression and family planning. Conclusion This is the first study to demonstrate the capability of an AI-based system to provide accurate and comprehensive answers to real-world patient queries in IBD. AI systems may serve as a useful adjunct for patients, in addition to standard of care in clinic and validated patient information resources. However, responses in specialist areas may deviate from evidence-based guidance and the replies need to give more firm advice.
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Rohrmeier, Martin. "On Creativity, Music’s AI Completeness, and Four Challenges for Artificial Musical Creativity." Transactions of the International Society for Music Information Retrieval 5, no. 1 (2022): 50–66. http://dx.doi.org/10.5334/tismir.104.
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Rohrmeier, Martin. "On Creativity Music's AI Completeness and Four Challenges for Artificial Musical Creativity." Transactions of the International Society for Music Information Retrieval 5, no. 1 (2022): 50–66. https://doi.org/10.5334/tismir.104.
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This article explores the notion of human and computational creativity as well as core challenges for computational musical creativity. It also examines the philosophical dilemma of computational creativity as being suspended between algorithmic determinism and random sampling, and suggests a resolution from a perspective that conceives of “creativity” as an essentially functional concept dependent on a problem space, a frame of reference (e.g. a standard strategy, a gatekeeper, another mind, or a community), and relevance. Second, this article proposes four challenges for artificial musical creativity and musical AI: (1) the <em>cognitive challenge</em> that musical creativity requires a model of music cognition, (2) the <em>challenge of the external world</em>, that many cases of musical creativity require references to the external world, (3) the <em>embodiment challenge</em>, that many cases of musical creativity require a model of the human body, the instrument(s) and the performative setting in various ways, (4) the <em>challenge of creativity at the meta-level</em>, that musical creativity across the board requires creativity at the meta-level. Based on these challenges it is argued that the general capacity of music and its creation fundamentally involves general (artificial) intelligence and that therefore musical creativity at large is fundamentally an AI-complete problem.
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Geneş, Muhammet, and Bülent Deveci. "A Clinical Evaluation of Cardiovascular Emergencies: A Comparison of Responses from ChatGPT, Emergency Physicians, and Cardiologists." Diagnostics 14, no. 23 (2024): 2731. https://doi.org/10.3390/diagnostics14232731.
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Background: Artificial intelligence (AI) tools, like ChatGPT, are gaining attention for their potential in supporting clinical decisions. This study evaluates the performance of ChatGPT-4o in acute cardiological cases compared to cardiologists and emergency physicians. Methods: Twenty acute cardiological scenarios were used to compare the responses of ChatGPT-4o, cardiologists, and emergency physicians in terms of accuracy, completeness, and response time. Statistical analyses included the Kruskal–Wallis H test and post hoc comparisons using the Mann–Whitney U test with Bonferroni correction. Results: ChatGPT-4o and cardiologists both achieved 100% correct response rates, while emergency physicians showed lower accuracy. ChatGPT-4o provided the fastest responses and obtained the highest accuracy and completeness scores. Statistically significant differences were found between ChatGPT-4o and emergency physicians (p < 0.001), and between cardiologists and emergency physicians (p < 0.001). A Cohen’s kappa value of 0.92 indicated a high level of inter-rater agreement. Conclusions: ChatGPT-4o outperformed human clinicians in accuracy, completeness, and response time, highlighting its potential as a clinical decision support tool. However, human oversight remains essential to ensure safe AI integration in healthcare settings.
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Rossi, Nicholas A., Kassandra K. Corona, Yuki Yoshiyasu, Dayton L. Young, and Brian J. McKinnon. "Evaluating the Accuracy and Completeness of Artificial Intelligence Responses Against Established Otology Guidelines." Otology & Neurotology Open 4, no. 3 (2024): e059. http://dx.doi.org/10.1097/ono.0000000000000059.
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Background: The incorporation of artificial intelligence (AI), especially large language models like Generative Pretrained Transformer 4 (GPT-4), into medical practice is a burgeoning field of interest. This research evaluates the applicability of GPT-4 in otology by analyzing its responses to queries based on otologic clinical practice guidelines. Methods: Key guidelines from otology were selected, and corresponding questions were formulated to examine GPT-4’s interpretation and response accuracy. Two independent reviewers assessed the AI-generated answers for accuracy and completeness, using a structured Likert scale. A re-evaluation was conducted to evaluate the reproducibility of the results. Results: The analysis showed a high accuracy level (mean score: 4.75 of 5) and completeness (mean score: 2.88 of 3) in GPT-4’s responses. The interrater agreement, as indicated by Cohen κ, was substantial. GPT-4 consistently advised on individualized treatment plans and professional consultation, particularly for guidelines with weaker evidence, demonstrating its cautious approach to handling medical information. Conclusion: GPT-4 exhibits promising potential as an auxiliary tool in otology, providing accurate and comprehensive information. However, its role should be viewed as supplementary, with emphasis on continual updates and careful monitoring to align with evolving medical knowledge. Future studies are recommended to further explore the depth of AI application in diverse clinical scenarios and its real-time impact on clinical outcomes.
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Bacardit, Jaume, Alexander Brownlee, Stefano Cagnoni, Giovanni Iacca, John McCall, and David Walker. "Evolutionary Computation and Explainable AI Towards ECXAI 2023: A Year in Review." ACM SIGEVOlution 16, no. 2 (2023): 1–6. http://dx.doi.org/10.1145/3610388.3610389.
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At GECCO 2022, we organized the first Workshop on Evolutionary Computation and Explainable AI (ECXAI). With no pretence at completeness, this paper briefly comments on its outcome, what has happened recently in the field, and our expectations for the near future and, in particular, for the upcoming second edition of EXCAI, at GECCO 2023.
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Chen, Po-Hsuan Cameron Cameron, Ji-Jung Jung, Yoona Kim, et al. "AI-assisted clinical summary and treatment planning for cancer care: A comparative study of human vs. AI-based approaches." Journal of Clinical Oncology 42, no. 16_suppl (2024): 1523. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.1523.
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1523 Background: Understanding a patient's clinical narrative, timeline, and history is critical for accurate treatment decision-making. However, reviewing and summarizing complex records is time-consuming and error-prone. Recent advancements in artificial intelligence (AI), specifically large language models (LLM), offer paths to improve quality and efficiency. Methods: A study was conducted on 50 breast cancer cases from an academic medical institution, utilizing all medical records—clinic, pathology, and radiology reports—up until the point of the initial treatment decision. All cases were processed using three different approaches: AI-assisted; full-AI; and human-only. In the AI-assisted method, two oncology physician assistants (PAs) revised AI-generated summaries to create clinical summaries. The full-AI method had AI independently produce clinical summaries, while the human-only method had the PAs compile summaries without AI. Eight board-certified international oncology specialists blindly evaluated summaries for faithfulness, completeness, and succinctness using a 3-point scale, ranked their preferences, and tried to predict which summaries were full-AI. Rankings were assessed using a Friedman test followed by a Wilcoxon signed-rank test, and full-AI prediction was assessed using a two-sided one-sample binomial test. After summarization, a distinct AI system with access to clinical guidelines provided treatment plans. These plans were then evaluated by a board-certified oncologist with access to the original treatment decision. Results: The study found specialists favored AI-assisted, followed by full-AI, and then human-only summaries, with average ranks of 1.73, 1.93, 2.34 respectively (lower is better, p<0.001). The difference between full-AI and AI-assisted was not significant (p=0.11). Evaluation scores (mean±95%CI, higher is better) showed AI-assisted, full-AI, and human-only scored 2.35±0.13, 2.14±0.14, 2.17±0.14 for faithfulness; 2.28±0.12, 2.01±0.12, 1.93±0.14 for completeness; and 2.33±0.12, 2.21±0.12, 1.99±0.13 for succinctness. The average summarization time was 19.71, 1.17, 26.03 minutes. Full-AI identification accuracy was 0.28 (not different from chance 0.33, p=0.46). With AI-assisted summaries, the treatment plans were accurate in 45 cases (90%) and partially accurate in 5 cases (10%). In the 5 partially accurate cases, the system was accurate with the provided input data, but there were inaccuracies with the input data, including incorrect formats or missing data. Conclusions: Incorporating LLMs into the creation of medical summaries has shown improvements in both quality and efficiency, achieving up to 22.2x speed up with full-AI, indicating that AI-assisted summarization tools can potentially enhance care quality. AI-assisted summaries yield accurate treatment plans when the input data is accurate.
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Oruçoğlu, Nurdan, and Elif Altunel Kılınç. "Performance of artificial intelligence chatbot as a source of patient information on anti-rheumatic drug use in pregnancy." Journal of Surgery and Medicine 7, no. 10 (2023): 651–55. http://dx.doi.org/10.28982/josam.7977.
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Background/Aim: Women with rheumatic and musculoskeletal disorders often discontinue using their medications prior to conception or during the few early weeks of pregnancy because drug use during pregnancy frequently results in anxiety. Pregnant women have reported seeking out health-related information from a variety of sources, particularly the Internet, in an attempt to ease their concerns about the use of such medications during pregnancy. The objective of this study was to evaluate the accuracy and completeness of health-related information concerning the use of anti-rheumatic medications during pregnancy as provided by Open Artificial Intelligence (AI's) Chat Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4, which are widely known AI tools. Methods: In this prospective cross-sectional study, the performances of OpenAI's ChatGPT versions 3.5 and 4 were assessed regarding health information concerning anti-rheumatic drugs during pregnancy using the 2016 European Union of Associations for Rheumatology (EULAR) guidelines as a reference. Fourteen queries from the guidelines were entered into both AI models. Responses were evaluated independently and rated by two evaluators using a predefined 6-point Likert-like scale (1 – completely incorrect to 6 – completely correct) and for completeness using a 3-point Likert-like scale (1 – incomplete to 3 – complete). Inter-rater reliability was evaluated using Cohen’s kappa statistic, and the differences in scores across ChatGPT versions were compared using the Mann–Whitney U test. Results: No statistically significant difference between the mean accuracy scores of GPT versions 3.5 and 4 (5 [1.17] versus 5.07 [1.26]; P=0.769), indicating the resulting scores were between nearly all accurate and correct for both models. Additionally, no statistically significant difference in the mean completeness scores of GPT 3.5 and GPT 4 (2.5 [0.51] vs 2.64 [0.49], P=0.541) was found, indicating scores between adequate and comprehensive for both models. Both models had similar total mean accuracy and completeness scores (3.75 [1.55] versus 3.86 [1.57]; P=0.717). In the GPT 3.5 model, hydroxychloroquine and Leflunomide received the highest full scores for both accuracy and completeness, while methotrexate, Sulfasalazine, Cyclophosphamide, Mycophenolate mofetil, and Tofacitinib received the highest total scores in the GPT 4 model. Nevertheless, for both models, one of the 14 drugs was scored as more incorrect than correct. Conclusions: When considering the safety and compatibility of anti-rheumatic medications during pregnancy, both ChatGPT versions 3.5 and 4 demonstrated satisfactory accuracy and completeness. On the other hand, the research revealed that the responses generated by ChatGPT also contained inaccurate information. Despite its good performance, ChatGPT should not be used as a standalone tool to make decisions about taking medications during pregnancy due to this AI tool’s limitations.
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Alqahtani, Saleh A., Reem S. AlAhmed, Waleed S. AlOmaim, et al. "Assessment of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction–associated steatotic liver disease." PLOS ONE 20, no. 2 (2025): e0317929. https://doi.org/10.1371/journal.pone.0317929.
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Background and aim Artificial intelligence (AI)-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have shown promising results in healthcare settings. These tools can help patients obtain real-time responses to queries, ensuring immediate access to relevant information. The study aimed to explore the potential use of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction–associated steatotic liver disease (MASLD). Methods An English patient questionnaire on MASLD was translated to Arabic. The Arabic questions were then entered into ChatGPT 3.5 on November 12, 2023. The responses were evaluated for accuracy, completeness, and comprehensibility by 10 Saudi MASLD experts who were native Arabic speakers. Likert scales were used to evaluate: 1) Accuracy, 2) Completeness, and 3) Comprehensibility. The questions were grouped into 3 domains: (1) Specialist referral, (2) Lifestyle, and (3) Physical activity. Results Accuracy mean score was 4.9 ± 0.94 on a 6-point Likert scale corresponding to “Nearly all correct.” Kendall’s coefficient of concordance (KCC) ranged from 0.025 to 0.649, with a mean of 0.28, indicating moderate agreement between all 10 experts. Mean completeness score was 2.4 ± 0.53 on a 3-point Likert scale corresponding to “Comprehensive” (KCC: 0.03–0.553; mean: 0.22). Comprehensibility mean score was 2.74 ± 0.52 on a 3-point Likert scale, which indicates the responses were “Easy to understand” (KCC: 0.00–0.447; mean: 0.25). Conclusion MASLD experts found that ChatGPT responses were accurate, complete, and comprehensible. The results support the increasing trend of leveraging the power of AI chatbots to revolutionize the dissemination of information for patients with MASLD. However, many AI-powered chatbots require further enhancement of scientific content to avoid the risks of circulating medical misinformation.
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Chen, Zhiyi, Xuerong Liu, Qingwu Yang, et al. "Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis." JAMA Network Open 6, no. 3 (2023): e231671. http://dx.doi.org/10.1001/jamanetworkopen.2023.1671.
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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Hatia, Arjeta, Tiziana Doldo, Stefano Parrini, et al. "Accuracy and Completeness of ChatGPT-Generated Information on Interceptive Orthodontics: A Multicenter Collaborative Study." Journal of Clinical Medicine 13, no. 3 (2024): 735. http://dx.doi.org/10.3390/jcm13030735.
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Background: this study aims to investigate the accuracy and completeness of ChatGPT in answering questions and solving clinical scenarios of interceptive orthodontics. Materials and Methods: ten specialized orthodontists from ten Italian postgraduate orthodontics schools developed 21 clinical open-ended questions encompassing all of the subspecialities of interceptive orthodontics and 7 comprehensive clinical cases. Questions and scenarios were inputted into ChatGPT4, and the resulting answers were evaluated by the researchers using predefined accuracy (range 1–6) and completeness (range 1–3) Likert scales. Results: For the open-ended questions, the overall median score was 4.9/6 for the accuracy and 2.4/3 for completeness. In addition, the reviewers rated the accuracy of open-ended answers as entirely correct (score 6 on Likert scale) in 40.5% of cases and completeness as entirely correct (score 3 n Likert scale) in 50.5% of cases. As for the clinical cases, the overall median score was 4.9/6 for accuracy and 2.5/3 for completeness. Overall, the reviewers rated the accuracy of clinical case answers as entirely correct in 46% of cases and the completeness of clinical case answers as entirely correct in 54.3% of cases. Conclusions: The results showed a high level of accuracy and completeness in AI responses and a great ability to solve difficult clinical cases, but the answers were not 100% accurate and complete. ChatGPT is not yet sophisticated enough to replace the intellectual work of human beings.
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Solomon, Thomas P. J., and Matthew J. Laye. "The sports nutrition knowledge of large language model (LLM) artificial intelligence (AI) chatbots: An assessment of accuracy, completeness, clarity, quality of evidence, and test-retest reliability." PLOS One 20, no. 6 (2025): e0325982. https://doi.org/10.1371/journal.pone.0325982.
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Background Generative artificial intelligence (AI) chatbots are increasingly utilised in various domains, including sports nutrition. Despite their growing popularity, there is limited evidence on the accuracy, completeness, clarity, evidence quality, and test-retest reliability of AI-generated sports nutrition advice. This study evaluates the performance of ChatGPT, Gemini, and Claude’s basic and advanced models across these metrics to determine their utility in providing sports nutrition information. Materials and methods Two experiments were conducted. In Experiment 1, chatbots were tested with simple and detailed prompts in two domains: Sports nutrition for training and Sports nutrition for racing. Intraclass correlation coefficient (ICC) was used to assess interrater agreement and chatbot performance was assessed by measuring accuracy, completeness, clarity, evidence quality, and test-retest reliability. In Experiment 2, chatbot performance was evaluated by measuring the accuracy and test-retest reliability of chatbots’ answers to multiple-choice questions based on a sports nutrition certification exam. ANOVAs and logistic mixed models were used to analyse chatbot performance. Results In Experiment 1, interrater agreement was good (ICC = 0.893) and accuracy varied from 74% (Gemini1.5pro) to 31% (ClaudePro). Detailed prompts improved Claude’s accuracy but had little impact on ChatGPT or Gemini. Completeness scores were highest for ChatGPT-4o compared to other chatbots, which scored low to moderate. The quality of cited evidence was low for all chatbots when simple prompts were used but improved with detailed prompts. In Experiment 2, accuracy ranged from 89% (Claude3.5Sonnet) to 61% (ClaudePro). Test-retest reliability was acceptable across all metrics in both experiments. Conclusions While generative AI chatbots demonstrate potential in providing sports nutrition guidance, their accuracy is moderate at best and inconsistent between models. Until significant advancements are made, athletes and coaches should consult registered dietitians for tailored nutrition advice.
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Azimi, Shelernaz, and Claus Pahl. "The Impact of Data Completeness and Correctness on Explainable Machine Learning Models." Journal of Data Intelligence 3, no. 2 (2022): 218–31. http://dx.doi.org/10.26421/jdi3.2-2.
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Many systems in the Edge Cloud, the Internet-of-Things or Cyber-Physical Systems are built for processing data, which is delivered from sensors and devices, transported, processed and consumed locally by actuators. This, given the regularly high volume of data, permits Artificial Intelligence (AI) strategies like Machine Learning (ML) to be used to generate the application and management functions needed. The quality of both source data and machine learning model is here unavoidably of high significance, yet has not been explored sufficiently as an explicit connection of the ML model quality that are created through ML procedures to the quality of data that the model functions consume in their construction. Here, we investigated the link between input data quality for ML function construction and the quality of these functions in data-driven software systems towards explainable model construction through an experimental approach with IoT data using decision trees.We have 3 objectives in this research: 1. Search for indicators that influence data quality such as correctness and completeness and model construction factors on accuracy, precision and recall. 2. Estimate the impact of variations in model construction and data quality. 3. Identify change patterns that can be attributed to specific input changes. This ultimately aims to support {\em explainable AI}, i.e., the better understanding of how ML models work and what impacts on their quality.
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Tsai, Cheng-En, Fanfan Chen, and Jane Yung-jen Hsu. "A Narrative Agent for the “Family Story Hoard”: An Information-Theoretic Framework for Interactive Storytelling." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 399–403. https://doi.org/10.1609/aaaiss.v5i1.35620.
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We introduce a narrative agent designed to facilitate the creation and preservation of personal stories within the "Family Story Hoard." The agent engages elderly users in interactive dialogues to elicit life stories, stores narrative elements, and evaluates the completeness of the story using information-theoretic metrics alongside Todorov’s five-stage narrative structure. The system dynamically guides users to fill narrative gaps through tailored prompts, ensures privacy by anonymizing sensitive data during storytelling, and automates archive with generated titles and chronological organization. By integrating conversational AI, information theory, and narrative analysis, this framework supports memory preservation while addressing challenges in coherence, completeness, and user privacy.
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Karataş, Özlem, Seden Demirci, Kaan Pota, and Serpil Tuna. "Assessing ChatGPT’s Role in Sarcopenia and Nutrition: Insights from a Descriptive Study on AI-Driven Solutions." Journal of Clinical Medicine 14, no. 5 (2025): 1747. https://doi.org/10.3390/jcm14051747.
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Background: Sarcopenia, an age-related decline in muscle mass and function, poses significant health risks. While AI tools like ChatGPT-4 (ChatGPT-4o) are increasingly used in healthcare, their accuracy in addressing sarcopenia remains unclear. Methods: ChatGPT-4’s responses to 20 frequently asked sarcopenia-related questions were evaluated by 34 experts using a four-criterion scale (relevance, accuracy, clarity, Ccmpleteness). Responses were rated from 1 (low) to 5 (high), and interrater reliability was assessed via intraclass correlation coefficient (ICC). Results: ChatGPT-4 received consistently high median scores (5.0), with ≥90% of evaluators rating responses ≥4. Relevance had the highest mean score (4.7 ± 0.5), followed by accuracy (4.6 ± 0.6), clarity (4.6 ± 0.6), and completeness (4.6 ± 0.7). ICC analysis showed poor agreement (0.416), with Completeness displaying moderate agreement (0.569). Conclusions: ChatGPT-4 provides highly relevant and structured responses but with variability in accuracy and clarity. While it shows potential for patient education, expert oversight remains essential to ensure clinical validity. Future studies should explore patient-specific data integration and AI comparisons to refine its role in sarcopenia management.
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Okoronkwo, Chizorom Ebosie. "Managerial Perceptions Of AI-Assisted Work Quality: A Protocol Report Of Communications Management Industries Perspective." IOSR Journal of Humanities and Social Science 30, no. 1 (2025): 19–25. https://doi.org/10.9790/0837-3001031925.
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This study investigates the use of ChatGPT for organizational tasks, focusing on how AI awareness influences managers' perceptions of quality and their evaluation of AI-assisted output. It reviews existing literature on human-machine co-creation, managerial perceptions of AI in the workplace, and communication management. The paper applies the D&M Information Systems Success Model and Total Quality Management theory to examine quality attributes such as relevance, accuracy, creativity, completeness, and alignment with organizational goals, guiding operational measures to assess managerial perceptions of quality in AI-assisted tasks. This study employs a mixed-methods experimental design to investigate how managers' awareness of AI capabilities affects their perceptions of quality and evaluation of employee work when using ChatGPT as a cocreator. It focuses on human-AI co-creation in communication-intensive industries (communication management) and addresses gaps in existing research. Particularly research in its analysis of the potential effects of AI on managerial perceptions. By integrating the TQM and ISS Models, the research aims to contribute to existing theory and provide a framework for evaluating AI-assisted outputs while clarifying the moderating role of managerial AI awareness on quality perceptions.
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Pugliese, Nicola, Davide Polverini, Rosa Lombardi, et al. "Evaluation of ChatGPT as a Counselling Tool for Italian-Speaking MASLD Patients: Assessment of Accuracy, Completeness and Comprehensibility." Journal of Personalized Medicine 14, no. 6 (2024): 568. http://dx.doi.org/10.3390/jpm14060568.
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Background: Artificial intelligence (AI)-based chatbots have shown promise in providing counseling to patients with metabolic dysfunction-associated steatotic liver disease (MASLD). While ChatGPT3.5 has demonstrated the ability to comprehensively answer MASLD-related questions in English, its accuracy remains suboptimal. Whether language influences these results is unclear. This study aims to assess ChatGPT’s performance as a counseling tool for Italian MASLD patients. Methods: Thirteen Italian experts rated the accuracy, completeness and comprehensibility of ChatGPT3.5 in answering 15 MASLD-related questions in Italian using a six-point accuracy, three-point completeness and three-point comprehensibility Likert’s scale. Results: Mean scores for accuracy, completeness and comprehensibility were 4.57 ± 0.42, 2.14 ± 0.31 and 2.91 ± 0.07, respectively. The physical activity domain achieved the highest mean scores for accuracy and completeness, whereas the specialist referral domain achieved the lowest. Overall, Fleiss’s coefficient of concordance for accuracy, completeness and comprehensibility across all 15 questions was 0.016, 0.075 and −0.010, respectively. Age and academic role of the evaluators did not influence the scores. The results were not significantly different from our previous study focusing on English. Conclusion: Language does not appear to affect ChatGPT’s ability to provide comprehensible and complete counseling to MASLD patients, but accuracy remains suboptimal in certain domains.
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Ningsih, Endang Suprihati, Herawati Herawati, Silvia Kristanti Tri Febriana, Edi Hartoyo, and Ardik Lahdimawan. "Bibliometric Analysis of Compliance and Completeness in Electronic Medical Record Filling: Trends and Insights from Hospital Documentation." Malahayati Nursing Journal 6, no. 9 (2024): 3626–42. http://dx.doi.org/10.33024/mnj.v6i9.16606.
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ABSTRACT The introduction of Electronic Medical Records (EMR) has revolutionized healthcare documentation, emphasizing the importance of compliance and completeness in medical data recording. This study aims to analyze the bibliometric data and visualize the publication trends related to compliance and completeness in EMR using VOSviewer. We employed a bibliometric approach to identify key clusters and relationships among terms in the literature from various journals and countries over recent years. The results highlight three main clusters: documentation and adherence, data completeness and quality, and accuracy and compliance in hospital settings. The analysis shows a significant global interest, with the USA, Germany, and Indonesia being major contributors. The study also reveals a rise in publications from 2018 to 2022, predominantly consisting of research articles. The most cited article, "Tools and Technologies for Registry Interoperability" by V. Ehrenstein et al., underscores the influence of registry interoperability on patient outcomes. Our conclusions indicate that while substantial progress has been made, further research is needed to explore factors affecting compliance and completeness across different healthcare systems, the long-term impact on patient outcomes, the integration of AI technologies, and real-time monitoring tools for data accuracy and compliance. Keywords: Electronic Medical Records, Compliance, Data Completeness, Bibliometric Analysis, Healthcare Documentation
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Nagayama, Misao. "On Boolean algebras and integrally closed commutative regular rings." Journal of Symbolic Logic 57, no. 4 (1992): 1305–18. http://dx.doi.org/10.2307/2275369.
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AbstractIn this paper we consider properties, related to model-completeness, of the theory of integrally closed commutative regular rings. We obtain the main theorem claiming that in a Boolean algebra B, the truth of a prenex Σn-formula whose parameters ai, partition B, can be determined by finitely many conditions built from the first entry of Tarski invariant T(ai)'s, n-characteristic D(n, ai)'s and the quantities S(ai, l) and S′(ai, l) for l < n. Then we derive two important theorems. One claims that for any Boolean algebras A and B, an embedding of A into B preserving D(n, a) for all a ϵ A is a Σn-extension. The other claims that the theory of n-separable Boolean algebras admits elimination of quantifiers in a simple definitional extension of the language of Boolean algebras. Finally we translate these results into the language of commutative regular rings.
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LaRosa, Sabrina, Angelo Cadiente, Natalia Dafonte, Leigh Michelle Plasil, and Jonathan Baum. "Creating a Baseline for Artificial Intelligence–Generated Obstetric Operative Reports: Analyzing ChatGPT 3.5 Generate Cesarean Birth Reports [ID 1189]." Obstetrics & Gynecology 145, no. 6S (2025): 37S. https://doi.org/10.1097/aog.0000000000005917.027.
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INTRODUCTION: While artificial intelligence (AI) is becoming more widely utilized throughout medicine, using ChatGPT to generate obstetric operative reports has yet to be examined. ChatGPT-3.5, an older model of ChatGPT, can serve as a baseline for AI efficacy in generating obstetric operative reports. This study examines completeness of cesarean birth operative reports generated by ChatGPT-3.5 to assess weaknesses and provide a baseline for ChatGPT obstetrical notes. METHODS: Twenty cesarean birth operative reports were generated using ChatGPT-3.5. Each note was evaluated for inclusion and completeness of history of present illness, operative findings, technique of resection, limits of resection, technique of reconstruction, and closure technique using a Likert scale. RESULTS: None of the 20 notes demonstrated completeness in any category. Brief history of present illness had a median score of 0. Operative findings, technique of resection, limits of resection, technique of reconstruction, and closure technique all lacked detail with a median score of 2. CONCLUSIONS/IMPLICATIONS: ChatGPT-3.5-generated cesarean birth operative reports demonstrated weaknesses in documenting all examined variables. The most concerning deficit was history of present illness, which was largely absent in generated reports. Gaps in detailed reports included operative findings, technique of reconstruction, limits of resection, technique of reconstruction, and closure technique. These findings highlight ChatGPT-3.5’s inadequacy in generating complete obstetric operative reports. Further research is needed to examine whether newer ChatGPT models address this gap.
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Adomako, Pascal, Talha Ali Khan, Raja Hashim Ali, and Rand Koutaly. "Comparison of Leading AI Models an Analytical Study of ChatGPT Google Bard and Microsoft Bing." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 14 (April 11, 2025): e31857. https://doi.org/10.14201/adcaij.31857.
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This comparative analysis delves into the capabilities of three prominent Conversational AI models: ChatGPT, Google Bard, and Microsoft Bing Chat. The study encompasses a meticulous exploration of their conversational skills, natural language processing abilities, and creative text generation. Methodologically, this study crafts a comprehensive evaluation framework, including complexity levels and tasks for each dimension. Through user-generated responses, key metrics of fluency, coherence, relevance, accuracy, completeness, informativeness, creativity, and relevance were assessed. The results reveal distinctive strengths of the AI models. The theoretical implications lead to recommendations for dynamic learning, ethical considerations, and cross-cultural adaptability. Practically, avenues for future research were proposed, including real-time user feedback integration, multimodal capabilities exploration, and collaborative human-AI interaction studies. The analysis sets the stage for benchmarking and environmental impact assessments, underlining the need for standardized metrics.
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Bondarenko, Olha, Maryna Utkina, Mykhailo Dumchikov, Daria Prokofieva-Yanchylenko, and Kateryna Yanishevska. "Review of the state anti-corruption institutions effectiveness in Ukraine." Revista Amazonia Investiga 10, no. 38 (2021): 219–33. http://dx.doi.org/10.34069/ai/2021.38.02.22.
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The objective of the article is a review of the state anti-corruption institutions' effectiveness in Ukraine. An important part of anti-corruption reform in Ukraine has been the complete transformation of anti-corruption institutions. That is why the authors try to use the most optimal methodology that would be able to ensure the comprehensiveness and completeness of the study: phenomenological; the descriptive; the hypothetic-deductive; the statistical; and the method of case law analysis. It is proposed to analyze the anti-corruption powers of general competence authorities in the sphere of anti-corruption. The authors analyzed the powers of specialized anti-corruption authorities' functioning: the National Agency on Corruption Prevention; the National Anti-Corruption Bureau; the National Agency for finding, tracing, and management of assets derived from corruption and other crimes; the Specialized Anti-Corruption Prosecutor’s Office and the High Anti-Corruption Court. The conclusion is drawn that, realizing the need of the anti-corruption task, the state has developed an extensive and relatively closed system of specialized anti-corruption authorities. Almost all bodies of state power and local self-government have acquired certain anti-corruption competencies. The authors made conclusion that such dispersion of powers, and in some cases their duplication, does not simplify, but rather complicates the effectiveness of combating corruption.
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Chernadchuk, Tamara, Inna Kozachok, Dmytro Maletov, Viktoriia Pankratova, and Alina Steblianko. "Organizational and legal provision of the control function of the local self-government body in conditions of sustainable development." Revista Amazonia Investiga 12, no. 62 (2023): 258–72. http://dx.doi.org/10.34069/ai/2023.62.02.26.
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The article discusses the issue of organizational and legal support of the control function of the local self-government body in the conditions of sustainable development in Ukraine. The functions of local self-government in Ukraine were outlined. The essence of the control function of local self-government bodies has been studied. The authors performed an analysis of the legislation defining the control powers of local self-government bodies and carried out its relationship with the goals of sustainable development. The purpose of this article is to define and characterize the control function of the local self-government body and its relationship with the goals of sustainable development. Various methods were used in the article, namely: historical method, method of documentary analysis, formal-legal, system-structural. Thus, with the help of the system-structural method, information on the relationship between the legally defined functions of local self-government bodies and the goals of sustainable development was systematized, which contributed to increasing the complexity, systematicity and completeness of the research. Thanks to this, it was possible to conclude that after the reform of the local self-government institute, which consisted in decentralization, the local self-government bodies gained additional control powers, which, as a result, strengthened sustainable development in the localities.
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Arunraju Chinnaraju. "Explainable AI (XAI) for trustworthy and transparent decision-making: A theoretical framework for AI interpretability." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 170–207. https://doi.org/10.30574/wjaets.2025.14.3.0106.
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Explainable Artificial Intelligence (XAI) has become a critical area of research in addressing the black-box nature of complex AI models, particularly as these systems increasingly influence high-stakes domains such as healthcare, finance, and autonomous systems. This study presents a theoretical framework for AI interpretability, offering a structured approach to understanding, implementing, and evaluating explainability in AI-driven decision-making. By analyzing key XAI techniques, including LIME, SHAP, and DeepLIFT, the research categorizes explanation methods based on scope, timing, and dependency on model architecture, providing a novel taxonomy for understanding their applicability across different use cases. Integrating insights from cognitive theories, the framework highlights how human comprehension of AI decisions can be enhanced to foster trust and reliability. A systematic evaluation of existing methodologies establishes critical explanation quality metrics, considering factors such as fidelity, completeness, and user satisfaction. The findings reveal key trade-offs between model performance and interpretability, emphasizing the challenges of balancing accuracy with transparency in real-world applications. Additionally, the study explores the ethical and regulatory implications of XAI, proposing standardized protocols for ensuring fairness, accountability, and compliance in AI deployment. By providing a unified theoretical framework and practical recommendations, this research contributes to the advancement of explainability in AI, paving the way for more transparent, interpretable, and human-centric AI systems.
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Al Barajraji, Mejdeddine, Sami Barrit, Nawfel Ben-Hamouda, et al. "AI-Driven Information for Relatives of Patients with Malignant Middle Cerebral Artery Infarction: A Preliminary Validation Study Using GPT-4o." Brain Sciences 15, no. 4 (2025): 391. https://doi.org/10.3390/brainsci15040391.
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Purpose: This study examines GPT-4o’s ability to communicate effectively with relatives of patients undergoing decompressive hemicraniectomy (DHC) after malignant middle cerebral artery infarction (MMCAI). Methods: GPT-4o was asked 25 common questions from patients’ relatives about DHC for MMCAI, twice over a 7-day interval. Responses were rated for accuracy, clarity, relevance, completeness, sourcing, and usefulness by board-certified intensivist* (one), neurologists, and neurosurgeons using the Quality Analysis of Medical AI (QAMAI) tool. Interrater reliability and stability were measured using ICC and Pearson’s correlation. Results: The total QAMAI scores were 22.32 ± 3.08 for the intensivist, 24.68 ± 2.8 for the neurologist, 23.36 ± 2.86 and 26.32 ± 2.91 for the neurosurgeons, representing moderate-to-high accuracy. The evaluators reported moderate ICC (0.631, 95% CI: 0.321–0.821). The highest subscores were for the categories of accuracy, clarity, and relevance while the poorest were associated with completeness, usefulness, and sourcing. GPT-4o did not systematically provide references for their responses. The stability analysis reported moderate-to-high stability. The readability assessment revealed an FRE score of 7.23, an FKG score of 15.87 and a GF index of 18.15. Conclusions: GPT-4o provides moderate-to-high quality information related to DHC for MMCAI, with strengths in accuracy, clarity, and relevance. However, limitations in completeness, sourcing, and readability may impact its effectiveness in patient or their relatives’ education.
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Gopinath Kathiresan. "Automated Test Case Generation with AI: A Novel Framework for Improving Software Quality and Coverage." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2880–89. https://doi.org/10.30574/wjarr.2024.23.2.2463.
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Modern software testing has become imperative as testing is being automated test case generation: it makes the test efficient, accurate and completely covered. Traditionally, scalability, adaptability, and completeness are the Achilles heels of scalability of traditional testing methods as manual and scripted. In this paper, we introduce a novel AI driven framework for automated test case generation based on deep learning and reinforcement learning using evolutionary algorithm to improve test case generation process. It provides an effective test coverage by dynamically generating and prioritizing test cases according to their historical data, execution patterns and real time software update. AI driven testing reduce manual effort, reduce test execution time, and detects defect earlier in the development cycle as per results. Although the challenge includes the quality of the data, the computing resource demand and application to the complex software system, the future advancement of AI and integration of AI & CI/CD pipelines will further increase the ability of automation in test case generation.
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Mock, Casey. "Tech CEOs, Trump and society’s new AI age." Australian Journalism Review 47, no. 1 (2025): 7–15. https://doi.org/10.1386/ajr_00176_7.
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The scene at Donald Trump’s second inauguration was a diorama of political power at the dawn of a new era. Across much of the globe, authority has shifted away from Washington, Brussels and London, away from heads of state and traditional titans of media and industry. But even before Elon Musk became Donald Trump’s right-hand man, 2025 was to be the year that a handful of American tech companies – Microsoft, Amazon, Google and Meta – achieved something unprecedented: concentrated control over virtually every aspect of how we communicate, consume information and understand reality. These companies determine what news we see, their algorithms shape our beliefs and their artificial intelligence systems increasingly generate the content that fills our screens. What is new in 2025 is the completeness of their dominance. As with similar epochal watersheds in 1789, 1848, 1917, 1946 and 1989, the implications will eventually touch every facet of life on the planet.
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Ha, Sukyeong, and Seumg-Hyun Kim. "Development of AI Convergence Picture Book Creation Class Program to Improve Creativity and AI Value Recognition for Elementary School Students." Korean Association For Learner-Centered Curriculum And Instruction 24, no. 24 (2024): 811–38. https://doi.org/10.22251/jlcci.2024.24.24.811.
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Objectives This study attempted to develop an artificial intelligence convergence picture book creation class program to improve creativity and AI value recognition for elementary school students and verify its effectiveness. Methods To this end, based on the ADDIE model, a total of 8 AI convergence picture book creation classes were developed, and an integrated creativity test and AI value recognition questionnaire were conducted for 41 sixth graders (20 experimental groups and 21 control groups) in elementary schools in Gyeongsangnam-do to verify their effectiveness. Quantitative and qualitative analysis was conducted to analyze the results, and follow-up measures of the class program were discussed. Results First, the artificial intelligence convergence picture book creation class program had statistically significant effects in the overall creative ability and some factors of creative ability compared to the existing picture book creation class program. Second, by comparing the two groups, it was confirmed that there was no statistically significant difference in AI value perception. Third, through qualitative analysis, it was confirmed that the artificial intelligence convergence picture book creation class program is effective in reducing the burden on learners in the process of conceiving and expressing ideas and increasing the completeness of the work. Conclusions This study is significant in that it provides examples of verifying the effectiveness of creative education using artificial intelligence and suggests ways to activate creative education using artificial intelligence.
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Kim, Phillip, and Junhee Youn. "Performance Evaluation of an Object Detection Model Using Drone Imagery in Urban Areas for Semi-Automatic Artificial Intelligence Dataset Construction." Sensors 24, no. 19 (2024): 6347. http://dx.doi.org/10.3390/s24196347.
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Modern image processing technologies, such as deep learning techniques, are increasingly used to detect changes in various image media (e.g., CCTV and satellite) and understand their social and scientific significance. Drone-based traffic monitoring involves the detection and classification of moving objects within a city using deep learning-based models, which requires extensive training data. Therefore, the creation of training data consumes a significant portion of the resources required to develop these models, which is a major obstacle in artificial intelligence (AI)-based urban environment management. In this study, a performance evaluation method for semi-moving object detection is proposed using an existing AI-based object detection model, which is used to construct AI training datasets. The tasks to refine the results of AI-model-based object detection are analyzed, and an efficient evaluation method is proposed for the semi-automatic construction of AI training data. Different FBeta scores are tested as metrics for performance evaluation, and it is found that the F2 score could improve the completeness of the dataset with 26.5% less effort compared to the F0.5 score and 7.1% less effort compared to the F1 score. Resource requirements for future AI model development can be reduced, enabling the efficient creation of AI training data.
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Kim, Seoyeon, and Yongkyung Cho. "A Study on the Importance of AI Smart Housing User Complaint Prevention Factors Using AHP." Residential Environment Institute Of Korea 21, no. 3 (2023): 37–47. http://dx.doi.org/10.22313/reik.2023.21.3.37.
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As interest in the 4th industrial revolution technology increases, the smart housing market where AI-based smart technology is grafted into cities is gradually growing. However, technological development is still being made centered on suppliers rather than users. Therefore, in this study, civil complaints that occurred while using the literature search and smart housing service were collected to analyze civil complaint elements, and the importance of civil complaint elements was derived through AHP. Through this, we tried to create a user-friendly base by preventing civil complaints about smart housing, which will become common in the future. 
 As a result of analyzing the importance of the first layer, the order of service access (0.2880) > operational support (0.2553) > physical environment establishment (0.2447) > cost expenditure (0.2120) appeared in the order. This means that in AI smart housing, the software part, such as smart housing technology such as IoT and service use, is the most important, followed by the humanware part, such as operation and related information management, and the hardware part, such as installing smart devices and facilities. Can be seen as. As a result of analyzing the 2nd layer importance, the top 5 factors were personal information management (0.1123) > difficulty in using service (0.0945) > poor construction (0.0750) > lack of completeness of service (0.0722) > incompatibility (0.0678). It shows that it is important to ensure that users do not feel that their personal information is being exposed or that they are being monitored, and that they should plan for convenient use of integrated controllers and apps. In addition, it shows that the construction of smart devices and facilities without defects and the completeness of partnership/individual services mounted on the smart housing platform are important, and it is necessary to select devices with good compatibility when building a physical environment.
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Tomar, Manish, Vasudevan Ananthakrishnan, and Muthuraman Saminathan. "Agentic AI-Powered Data Quality Guardians for Regulated Industries." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 507–31. https://doi.org/10.60087/jaigs.v3i1.378.
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In regulated industries such as healthcare, finance, and pharmaceuticals, ensuring data quality is not merely a matter of efficiency but of compliance, trust, and safety. This paper introduces the concept of Agentic AI-Powered Data Quality Guardians—autonomous, intelligent agents designed to proactively monitor, assess, and enhance data quality across complex and evolving systems. Leveraging advancements in agentic artificial intelligence (AI), these digital guardians operate with minimal human oversight, employing reasoning, learning, and self-correction to maintain data integrity in real time. The proposed framework combines rule-based validation, anomaly detection, semantic enrichment, and regulatory alignment to ensure compliance with stringent industry standards. Case studies and simulations demonstrate the effectiveness of these agents in improving data accuracy, completeness, and consistency, thereby reducing operational risk and audit failure. This research underscores the transformative potential of agentic AI in modernizing data governance and fostering a resilient, compliant data ecosystem in high-stakes sectors.
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Mustofa, Hisbulloh Als, Muhammad Roil Bilad, and Nuraqilla Waidha Bintang Grendis. "Utilizing AI for Physics Problem Solving: A Literature Review and ChatGPT Experience." Lensa: Jurnal Kependidikan Fisika 12, no. 1 (2024): 78. http://dx.doi.org/10.33394/j-lkf.v12i1.11748.
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The integration of artificial intelligence (AI) tools in physics education is gaining traction, driven by their potential to enhance learning experiences and outcomes. This study aims to investigate the use of AI tools, particularly ChatGPT, in solving physics problems and enhancing educational practices. Utilizing a systematic literature review following PRISMA guidelines, the research identifies current trends and practical applications of AI in physics education. The results indicate that AI tools effectively support lesson planning, introduce innovative teaching methodologies, and assist in solving complex physics problems, significantly enhancing problem-solving skills and personalized learning experiences. However, challenges such as inaccuracies in handling advanced content, the lack of useful visual aids, and the need for human intervention to ensure the completeness and accuracy of AI-generated content were noted. Personal experiences, supplemented by an interview with a thermodynamics lecturer, revealed that while ChatGPT can simplify complex concepts and improve comprehension, it could not replace the mentorship and nuanced feedback provided by human educators. The study concludes with recommendations for integrating AI tools into physics education, emphasizing the need for balanced integration with traditional teaching methods, improved AI literacy among educators and students, and future developments focusing on personalized learning and enhanced visualization capabilities. The findings demonstrate the transformative potential of AI in physics education and highlight the importance of addressing its limitations to maximize educational outcomes.
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Koontz, Michael Zachary, Ayse Tezcan, Julie Latham, Phyllis Ortiz, Joseph Nathaniel Paulson, and Annette Campbell Fontaine. "Abstract B051: Improving Race and Ethnicity Data Completeness Using a Concise Survey Designed to Collect Social Determinants of Health (SDoH) Data: Results from Two Community Oncology Practices." Cancer Epidemiology, Biomarkers & Prevention 33, no. 9_Supplement (2024): B051. http://dx.doi.org/10.1158/1538-7755.disp24-b051.
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Abstract Background: Race and ethnicity (R/E) information is crucial for understanding health disparities and the efficacy/effectiveness of treatments across different patient groups. However, these data are often missing or inaccurate in EHR. Literature suggests that surveys can improve upon these gaps in EHR R/E data. FDA recently issued guidance on collecting and reporting R/E data using standardized terminology, which should be applied to demographic data in new clinical trials and studies. N-Power Medicine (NPM) developed a concise survey, incorporating FDA’s R/E recommendations, to collect SDoH data as part of its registry (Kaleido) enhanced oncology network platform for quality data collection. Here, we report the initial R/E findings from this survey. Methods: Patients participating in the Kaleido Registry at 2 community oncology practices across different US regions completed the survey during their clinic visits with assistance from NPM onsite staff. The survey allowed multiple race selections to include mixed races and a free-text option for “Other”. R/E data from the same patients were also extracted from EHRs. Completeness was reported as the percentage of informative answers: any race, other, and chose not to disclose. “Unknown” and “Not available” were missing data. Results: Between November 11, 2023 and April 30, 2024, 1109 patients completed the questionnaire with over 99% response rate. Both sites contributed equally and had similar patient demographics: about 61% female and 79% aged 60 or above. The large majority of patients had breast cancer followed by lung and colorectal cancers. Survey completeness was 96.7% for race and 99.6% for ethnicity, as compared with EHR completeness of 78.6% for race and 91.1% for ethnicity. 21% of EHR data for race was coded as "Unknown and Not Available. About 20% of the survey respondents were Hispanic or Latino. Both sites’ EHR data included expected R/E categories except for one site having 21 additional unique race entries. The majority of survey participants were White (W, 75.7%) followed by Asian (A, 3.6%), American Indian or Alaska Native (AI/AN, 3.2%), Black or African American (B/AA, 2.4%), Native Hawaiian or Pacific Islander (NH/PI, 1.0%) and multiple-selection (2.9%; Other and W (8), AI/AN (3), A (2); W and AI/AN (10), A (7); A and AI/AN (1), W and B (1)). 11% of responses were Other, which included various races (15% European/Caucasian), nationalities, ethnicities (49% Hispanic) and religions, hence, could be further disambiguated. We will report details regarding the survey respondent’s other SDoH. Conclusions: We successfully implemented a standardized questionnaire, incorporating FDA R/E collection recommendations, at two community oncology sites. The results demonstrate that the NPM R/E questionnaire has higher completeness compared to EHR data, allowing better assessment of the impact of R/E on clinical trial enrollment, genomic testing utilization, treatment patterns, and outcomes. This improvement is vital for understanding and addressing health disparities in oncology care. Citation Format: Michael Zachary Koontz, Ayse Tezcan, Julie Latham, Phyllis Ortiz, Joseph Nathaniel Paulson, Annette Campbell Fontaine. Improving Race and Ethnicity Data Completeness Using a Concise Survey Designed to Collect Social Determinants of Health (SDoH) Data: Results from Two Community Oncology Practices [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B051.
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Easouh, Feda Yousef Jeries, Thelal Eqab Oweis, and Hanada Ahmad Makahleh. "A Bibliometric Lens on the Future: How AI continues to transform education institutions." Human Systems Management 43, no. 6 (2024): 825–44. https://doi.org/10.1177/01672533241305172.
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BACKGROUND: This extensive analysis examines the changing environment of AI in education using 1,480 Web of Science research publications. The goal is to map the academic landscape, highlight significant topics, and identify notable authors, nations, and documents to enable future study in this expanding subject. OBJECTIVE: The study seeks to explore AI’s influence on education and identify patterns and insights that might inform future research. METHODS: Bibliometric and content analytics are used to carefully extract data from the Web of Science Core Collection. To ensure completeness and relevance, 1,480 peer-reviewed papers from 2008 to 2023 were selected. RESULTS: The study identified six research clusters: AI Ethics and Innovation, Teaching Systems, Learning Experiences, Education Performance Enhancement via AI, Sustainable Development Goals in Education, and AI, Big Data, and Education. With the help of 107 universities, 310 keywords, and 160 authors from 37 different countries, these clusters are thriving islands in their respective fields. IMPLICATIONS: This study helps researchers, educators, and policymakers explore the literature and identify prospective research areas. It allows stakeholders to lead AI in education towards a more inclusive and enlightened future.
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Boot, Walter, and Xin Yao Lin. "HARNESSING ADVANCES IN ARTIFICIAL INTELLIGENCE TO SUPPORT AN AGING POPULATION." Innovation in Aging 8, Supplement_1 (2024): 279–80. https://doi.org/10.1093/geroni/igae098.0910.
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Abstract We are witnessing unprecedented growth in the sophistication of artificial intelligence (AI) and its rapid diffusion into all aspects of our lives. This session will focus on the potential and pitfalls of using AI to support the health, wellbeing, and quality of life of older people. Dr. Clara Berridge will contribute a big-picture discussion of outstanding ethical issues in AI, including large language model (LLM) tools, and provide an organized framework to promote the benefits of AI while minimizing ethical concerns. Dr. Shelia Cotten will discuss the integration of AI in healthcare, focusing on stakeholder acceptance and ethical concerns, and present suggestions to advance AI acceptance based on a scoping review of AI-enabled devices in diagnosis, treatment, and rehabilitation settings. Dr. Catherine Diaz-Asper will address the ethical challenges of language technologies in AI, highlighting issues of transparency, equity, and bias, and propose methods to boost user trust, particularly among older adults, through stakeholder co-design and user-friendly interfaces. Zhimeng Luo, MS, will explore the capabilities of large multimodal models (LMMs) to interpret healthcare infographics for dementia caregivers, demonstrating their accuracy and completeness and suggesting implications for content generation and health education material design. Dr. Lin Jiang will examine the use of artificial intelligence in supporting Alzheimer’s and dementia family caregivers, presenting findings from mixed-methods research that evaluates AI’s role in providing emotional and social support, and discussing the importance of interdisciplinary collaboration and caregiver involvement in AI development.
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Yılmaz Muluk, Selkin, Vedat Altuntaş, and Zehra Duman Şahin. "A New Approach: Generative Artificial Intelligence in Physiatry Resident Education." Medical Records 7, no. 1 (2024): 120–28. https://doi.org/10.37990/medr.1581104.
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Aim: This study assessed the effectiveness of ChatGPT-4o, an artificial intelligence (AI) platform, in creating a therapeutic exercises presentation for physiatry residents’ education. The aim was to compare the quality of content created by ChatGPT-4o with that of an expert, exploring the potential of AI in healthcare education. Material and Method: Both an expert and AI created 24 PowerPoint slides across six topics, using same reputable sources. Two other experts assessed these slides according to CLEAR criteria: completeness, lack of false information, appropriateness, and relevance and scored as excellent, 5; very good=4, good=3, satisfactory/fair=2, or poor, 1. Results: Interrater reliability was confirmed. Average scores (calculated from the two raters’ scores) for each topic were significantly lower for AI than for the expert, although whole presentation scores did not differ between the two. Overall scores (calculated from the average scores of all items) for each topic were good to excellent for AI, excellent for the expert. The overall score for whole presentation was good for AI, excellent for the expert. Highest ranks for individual criteria was relevance for AI, lack of false information for the expert. Some AI-generated elements were later integrated into the expert work, enhancing the content. Conclusion: ChatGPT-4o can generate effective educational content, though expert outperforms it, highlighting the need for professional oversight. Collaboration between humans and AI may further enhance educational outcomes.