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Journal articles on the topic 'AI in Clinical research'
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Dietzel, Matthias, and Pascal A. T. Baltzer. "Bridging AI research and clinical impact." European Journal of Radiology Artificial Intelligence 2 (June 2025): 100026. https://doi.org/10.1016/j.ejrai.2025.100026.
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Topol, Eric J. "Welcoming new guidelines for AI clinical research." Nature Medicine 26, no. 9 (2020): 1318–20. http://dx.doi.org/10.1038/s41591-020-1042-x.
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Park, Yoonyoung, Gretchen Purcell Jackson, Morgan A. Foreman, Daniel Gruen, Jianying Hu, and Amar K. Das. "Evaluating artificial intelligence in medicine: phases of clinical research." JAMIA Open 3, no. 3 (2020): 326–31. http://dx.doi.org/10.1093/jamiaopen/ooaa033.
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Abstract Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical trial phases for drugs and medical devices, and we present study design and methodological guidance for each stage.
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Yang, Wei-Hua, and Yan-Wu Xu. "Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023)." International Journal of Ophthalmology 16, no. 9 (2023): 1361–72. http://dx.doi.org/10.18240/ijo.2023.09.02.
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With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.
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Endo, Yutaka, Laura Alaimo, Giovanni Catalano, Odysseas P. Chatzipanagiotou, and Timothy M. Pawlik. "Application of artificial intelligence to hepatobiliary cancer clinical outcomes research." Artificial Intelligence Surgery 4, no. 2 (2024): 59–67. http://dx.doi.org/10.20517/ais.2024.09.
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The rapid evolution of modern technology has made artificial intelligence (AI) an important emerging tool in healthcare. AI, which is a broad field of computer science, can be used to develop systems or machines equipped with the ability to tackle tasks that traditionally necessitate human intelligence. AI can be used to perform multifaceted tasks that involve the synthesis of large amounts of data with the generation of solutions, algorithms, and decision support tools. Various AI approaches, including machine learning (ML) and natural language processing (NLP), are increasingly being used to analyze vast healthcare datasets. In addition, visual AI has the potential to revolutionize surgery and the intraoperative experience for surgeons through augmented reality enhancing surgical navigation in real-time. Specific applications of AI in hepatobiliary tumors such as hepatocellular carcinoma and biliary tract cancer can improve patient diagnosis, prognostic risk stratification, as well as treatment allocation based on ML-based models. The integration of radiomics data and AI models can also improve clinical decision making. We herein review how AI may be of particular interest in the care of patients with complex cancers, such as hepatobiliary tumors, as these patients often require a multimodal treatment approach.
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Cunningham, M., and M. Dowsett. "Highlights and Future Directions in AI Clinical Research." MD Conference Express 14, no. 56 (2014): 16. http://dx.doi.org/10.1177/1559897715572118.
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Rao, Sohail. "Artificial Intelligence vs. Evidence-Based Clinical Trials in Humans: A Paradigm Shift in Clinical Research." INNOVAPATH 2, Q2 (2025): 6. https://doi.org/10.63501/xptwta38.
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Prospective interventional clinical studies in humans remain the cornerstone of evidence-based medicine, guiding clinical decision-making and regulatory approvals. However, with the rapid advancement of Artificial Intelligence (AI) and the conceptual emergence of Artificial General Intelligence (AGI), the medical community is beginning to explore whether these technologies can augment or even replace traditional clinical trials. This manuscript critically examines the capabilities of AI and AGI in simulating, predicting, and evaluating clinical interventions. We discuss the methodological, ethical, and regulatory considerations of such a paradigm shift. While AI shows promise in retrospective analyses, clinical decision support, and trial optimization, replacing prospective interventional trials remains beyond current technological and ethical limits. Though theoretically more capable, AGI introduces concerns of explainability, bias propagation, and validation challenges. The future of clinical trials may lie in hybrid models that integrate AI with traditional methodologies, enhancing efficiency without compromising scientific rigor.
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Sevier, Brian, Daniella Meeker, Christine Chaisson, Roberta Bruhn, and Eric Borchardt. "556 Transforming clinical research administration: The role of generative AI and chatbots." Journal of Clinical and Translational Science 9, s1 (2025): 162–63. https://doi.org/10.1017/cts.2024.1127.
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Objectives/Goals: To explore how generative AI and chatbot technologies can transform clinical research administration by improving operational efficiency, reducing administrative burden, and thereby enhancing overall productivity and accuracy in clinical research environments. Methods/Study Population: This explores AI’s application in enhancing clinical research administration. We specifically address AI’s role in QCT/MCA activities, charge master data cleaning, and generating IRB consent forms from award documents. AI algorithms optimize charge master data for accuracy and compliance. Generative AI models are employed to produce IRB consent forms efficiently, incorporating key grant documents. AI also conducts thematic analyses of historical CTSA aims to identify trends and recurring themes. Furthermore, AI-assisted tools enhance study design through innovative approaches to hypothesis generation, sample size calculation, and protocol development. Integrating these AI methods aims to significantly improve efficiency, accuracy, and overall quality in clinical research administration. Results/Anticipated Results: Incorporating AI into clinical research administration will yield improvements in efficiency and accuracy. AI-driven QCT/MCA steps are expected to reduce human error and enhance data integrity. Chargemaster data cleaning via AI prompts will likely result in optimized, error-free data, ensuring compliance with regulations. The use of genAI for creating IRB consent forms from grant documents should significantly streamline the IRB approval process, reducing preparation time and administrative burdens. Thematic analysis of CTSA aims by AI will provide deep insights into historical trends and recurring themes, aiding in strategic planning. AI-assisted study design tools are anticipated to optimize sample estimation, protocol development, and advance the quality of clinical research administration. Discussion/Significance of Impact: The significance lies in enhancing efficiency, accuracy, and quality in clinical research administration. By streamlining processes, reducing errors, and providing strategic insights, AI supports the CTSA mission to accelerate translational research, thus improving public health outcomes and scientific innovation.
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Researcher. "AI-DRIVEN DATA INTEGRATIONS AND AUTOMATIONS FOR CLINICAL RESEARCH OPERATIONS (CRO)." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2140–49. https://doi.org/10.5281/zenodo.14348930.
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The complexity of modern clinical research operations (CRO) necessitates advanced solutions for managing vast datasets, streamlining workflows, and ensuring compliance with stringent regulatory requirements. AI-driven data integration and automation technologies have emerged as key enablers of efficiency and accuracy in CRO, facilitating real-time data synchronization, workflow automation, and predictive analytics. This comprehensive article examines the current challenges in clinical research operations, explores AI-driven solutions for data integration, evaluates automation technologies, and assesses the benefits of AI integration in CRO. The article also discusses critical implementation considerations, including technical infrastructure requirements and change management strategies, while highlighting how AI technologies transform traditional clinical research processes through improved operational efficiency, enhanced data quality, and streamlined regulatory compliance
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Kimura-Ono, Aya, and Takuo Kuboki. "AI research in dentistry and related clinical-study design." Okayama Igakkai Zasshi (Journal of Okayama Medical Association) 133, no. 3 (2021): 184–88. http://dx.doi.org/10.4044/joma.133.184.
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Hamamoto, Ryuji. "3. Medical AI Research and Development for Clinical Application." Nihon Naika Gakkai Zasshi 112, no. 9 (2023): 1636–42. http://dx.doi.org/10.2169/naika.112.1636.
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Urman, Alexandra, Ching-Kun Wang, Irene Dankwa-Mullan, Ethan Scheinberg, and Michael J. Young. "Harnessing AI for health equity in oncology research and practice." Journal of Clinical Oncology 36, no. 30_suppl (2018): 67. http://dx.doi.org/10.1200/jco.2018.36.30_suppl.67.
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67 Background: Recent advances in artificial intelligence (AI) carry underexplored practical and ethical implications for the practice of clinical oncology. As oncologic applications of AI proliferate, a framework for guiding their ethical implementations and equitable distribution will be crucial. Methods: We reviewed the current landscape of AI applications in oncology research and clinical practice by reviewing the current body of evidence in PubMed and Medline. Key ethical challenges and opportunities to address health equity are critically evaluated and highlighted. Ethical implications for patients, clinicians and society at large are delineated, with particular focus on the impact and ramifications of AI with respect to healthcare disparities and equity of oncology care delivery. Results: Growing concerns that AI may widen disparities in oncologic care by virtue of lack of affordability, inconsistent accessibility and biased machine-learning models are addressed. Although there is potential for AI to widen disparities in oncology care, using foresight in application, AI has the potential to (1) democratize access to specialized clinical knowledge, (2) improve the accuracy of predicting cancer susceptibility, recurrence and mortality, (3) prevent diagnostic errors in under-resourced settings, (4) minimize unintended bias and (5) enable access to tailored therapeutic options including clinical trials if appropriately deployed. Separately, AI can be harnessed to identify areas of underserved needs and optimize systems of health-information sharing and reimbursements as blockchain technology converges with AI. As AI advances it will have a larger presence in oncology research and clinical practice. Conclusions: A strategic framework integrating ethical standards and emphasizing equitable implementation can help ensure that the potential of AI to address disparities in oncology are maximally captured and its perils averted. Further work is being done on exploring these challenges and will be submitted as a manuscript.
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Salybekov, Amankeldi A., Markus Wolfien, Waldemar Hahn, Sumi Hidaka, and Shuzo Kobayashi. "Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes." Biomedicines 12, no. 3 (2024): 606. http://dx.doi.org/10.3390/biomedicines12030606.
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The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care.
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Soltani, Madjid, Farshad Moradi Kashkooli, Mohammad Souri, et al. "Enhancing Clinical Translation of Cancer Using Nanoinformatics." Cancers 13, no. 10 (2021): 2481. http://dx.doi.org/10.3390/cancers13102481.
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Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.
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Schuurmans, Megan, Natália Alves, Pierpaolo Vendittelli, Henkjan Huisman, and John Hermans. "Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging." Cancers 14, no. 14 (2022): 3498. http://dx.doi.org/10.3390/cancers14143498.
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Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
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Bhattamisra, Subrat Kumar, Priyanka Banerjee, Pratibha Gupta, Jayashree Mayuren, Susmita Patra, and Mayuren Candasamy. "Artificial Intelligence in Pharmaceutical and Healthcare Research." Big Data and Cognitive Computing 7, no. 1 (2023): 10. http://dx.doi.org/10.3390/bdcc7010010.
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Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.
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Lee, Ellen, Helmet Karim, Ipsit Vahia, and Andrea Iaboni. "100 - Artificial Intelligence in Geriatric Mental Health: Recent Advances in Clinical Research." International Psychogeriatrics 33, S1 (2021): 1. http://dx.doi.org/10.1017/s1041610221001307.
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SynopsisWith the rise of wearable sensors, advancement in comprehensible artificial intelligence (AI) algorithms, and growing acceptance of AI in medicine, AI has great potential to more reliably diagnose, prognose, and treat mental illnesses. The rapidly rising number of older adults worldwide presents a unique challenge for clinicians due to increased mental health needs in the setting of a dwindling clinical workforce. AI has enabled researchers to better understand mental illnesses by taking advantage of ‘big data.’This symposium will present an overview of novel research leveraging AI (machine learning, natural language processing) to better track, understand, and support mental health and cognitive functioning in older adults.Helmet Karim, PhD will present on prediction of treatment response in late-life major depressive disorder and the implications of those models.Ellen Lee, MD will present on using natural language processing to understand psychosocial functioning in older adults.Ipsit Vahia, MD will present on radio-based sensors to phenotype changes in behavior patterns that may correlate with a range of geropsychiatric symptoms.Andrea Iaboni, MD DPhil FRCPC will present on multimodal wearable and vision-based sensors for the detection and categorization of behavioural symptoms of dementia.The symposium includes three physician-scientists (Iaboni, Lee, Vahia), two women (Iaboni, Lee), and two early career faculty (Lee, Karim – co-chairs). The symposium represents four different institutions across the country (McLean/Harvard, Toronto Rehabilitation Institute/University of Toronto, UC San Diego, University of Pittsburgh) and four very different approaches using AI technology to improve understanding and outcomes in the field of geriatric mental health.The symposium seeks to address the underutilization of AI in psychiatric research, especially in the field of aging research. The increased individual-level heterogeneity associated with aging; complex trajectories of decline in cognitive, mental, and physical health; and lack and slow adoption of older adult-centered technologies present great challenges to advancing the field. However, advances in the field of explainable AI and transdisciplinary development of AI approaches can address the unique challenges of aging research.
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Raja, Rohit Singh. "The Impact of Artificial Intelligence on Clinical Research and Healthcare: A Systematic Review." International Scientific Journal of Engineering and Management 03, no. 09 (2024): 1–7. https://doi.org/10.55041/isjem01962.
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Artificial intelligence (AI) is revolutionizing healthcare by enhancing clinical research and patient care. AI employs techniques such as machine learning and deep learning to process extensive datasets efficiently. This enables early diagnoses, personalized treatments, and efficient resource management. AI and its applications extend to drug development, diagnostic imaging, operational efficiency, and public health surveillance. The COVID-19 pandemic further showcased AI’s potential in vaccine development and outbreak monitoring, emphasizing its integral role in reshaping healthcare systems globally. Objective: The objective of this review is to evaluate the methodologies, findings, and challenges in studies on AI applications in healthcare and resource management. It aims to identify trends, gaps, and barriers to successful AI integration and implementation.
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Lehman, C., B. Dontchos, and LR Lamb. "Abstract ES7-3:/AI." Cancer Research 82, no. 4_Supplement (2022): ES7–3—ES7–3. http://dx.doi.org/10.1158/1538-7445.sabcs21-es7-3.
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Abstract The tools of AI offer promise to uncover and amplify data, previously hidden in images of the human body, and expand the field of radiomics to more accurately predict breast cancer risk across diverse patient populations. Although deep learning products trained to predict mammographic breast density and risk of breast cancer are now available, their performance in routine clinical settings is largely unknown. Since the creation of the Gail model in 1989, risk models have supported risk-adjusted screening and prevention, and their continued evolution has been a central pillar of breast cancer research. Mammographic breast density is now being incorporated into well-established clinical risk models, which are used to determine eligibility for supplemental imaging and other services for patients at increased risk. However, mammographic breast density assessment is subjective and varies widely between and within radiologists. For example, Sprague et al demonstrated considerable variability of qualitative Breast Imaging Reporting and Data System (BI-RADS) density assessments with a range of 6% to 85% of mammograms being assessed as heterogeneously or extremely dense across 83 radiologists. To limit variability and subjectivity, various automated breast density assessment methods were commercially developed, but clinical evaluation has yielded mixed results. Density is only one limited feature of any woman’s mammogram. Deep learning models can operate over the full resolution of mammogram images to assess a patient’s future breast cancer risk. Rather than manually identifying discriminative image patterns, machine learning models can discover these patterns directly from the data. Specifically, models are trained with full resolution mammograms and the outcome of interest, namely whether the patient developed breast cancer within five years from the date of the examination. Our recent work demonstrates that application of novel artificial intelligence applications to imaging data can significantly improve breast density assessment and breast cancer risk prediction. In addition, unlike traditional models, our DL models perform equally well across varied races, ages, and family histories and we have built a clinical platform which is currently in use to support implementation of our density and risk models into routine clinical care. From this clinical platform, we found that use of a deep learning model more accurately predicts mammographic breast density, reducing human variation and “overcalling” of breast density, and can help healthcare systems more appropriately utilize limited supplemental screening resources as well as provide patients with more accurate information regarding their breast density. We have also found a DL breast cancer risk model, generated in routine clinical mammography screening programs, can predict future breast cancer risk and can more accurately identify women who will most benefit from screening mammography. This DL score can be used in diverse clinical settings that currently depend on traditional risk scores, which are at best modest in performance. DL risk scores based on the mammogram alone can inform shared decision making with patients and their healthcare providers regarding more personalized risk reduction and early detection strategies. Citation Format: C Lehman, B Dontchos, LR Lamb./AI [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr ES7-3.
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G, Nihalani. "Applications of Artificial Intelligence in Pharmaceutical Research: An Extensive Review." Bioequivalence & Bioavailability International Journal 8, no. 1 (2024): 1–7. http://dx.doi.org/10.23880/beba-16000231.
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Artificial Intelligence (AI) is transforming the pharmaceutical industry, revolutionizing drug discovery, development, and patient care. This comprehensive review explores the diverse applications of AI in the pharmaceutical sector, discussing its advantages, challenges, and future prospects. AI-driven techniques, including machine learning and deep learning, have accelerated drug discovery processes by expediting target identification, virtual screening, and drug repurposing. Predictive analytics optimize clinical trial design, while personalized medicine leverages patient-specific data for precise treatment plans. AI enhances drug formulation and manufacturing, improves pharmacovigilance and drug safety, and supports drug pricing and market access strategies. Despite its potential, challenges such as ethical considerations and data privacy concerns must be addressed. The integration of AI into existing workflows and regulatory compliance remain areas of focus. By overcoming these challenges, AI stands poised to reshape the pharmaceutical industry and pave the way for a more efficient, personalized, and impactful approach to drug development and healthcare.
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Pandey, Ashish. "“The Impact of Artificial Intelligence in Dentistry: Transforming Clinical and Laboratory Research”." Journal of Clinical and Laboratory Research 7, no. 8 (2024): 01–16. https://doi.org/10.31579/2768-0487/153.
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Artificial Intelligence (AI) is revolutionizing healthcare, with dentistry as no exception. The integration of AI technologies into both clinical and laboratory aspects of dental research has the potential to drastically improve diagnostics, treatment planning, and patient outcomes. This article provides an in-depth exploration of AI's applications in dentistry, focusing on the latest advancements that are shaping clinical and laboratory research. From AI-driven diagnostic tools, such as machine learning algorithms for detecting oral pathologies, to virtual assistants in prosthodontic and orthodontic treatments, AI has become a game-changer. Furthermore, AI's application in laboratory research, such as materials development and biomimetic modeling, is evolving rapidly, offering the potential for personalized treatment options. Despite the numerous benefits, there are ethical, regulatory, and technical challenges that must be addressed to ensure AI's responsible and effective use in the dental field. This paper aims to discuss these developments and present the latest scientific contributions to the understanding and application of AI in dentistry, providing a critical analysis of its transformative potential in both clinical practice and research laboratories.
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Fokunang, Charles Ntungwen, Marceline Djuidje Ngounoue, Joseph Fokam, et al. "Artificial Intelligence in Medical Research: Ethical and Regulatory Challenges in Developing Economies." Journal of Advances in Medical and Pharmaceutical Sciences 27, no. 6 (2025): 63–89. https://doi.org/10.9734/jamps/2025/v27i6788.
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Introduction: Clinical research is a key area in which the use of AI in healthcare data seen a significant increase, even though met with great ethical, legal and regulatory challenges. Artificial Intelligence (AI) concerns the ability of algorithms encoded in technology to learn from data, to be able to perform automated tasks without every step in the process being explicitly to be programmed by a human. AI development relies on big data collected from clinical trials to train algorithms, that requires careful consideration of consent, data origin and ethical standards. When data is acquired from third-party sources, transparency about collection methods, geographic origin and anonymization standards becomes critical. While consent forms used in clinical trials can offer clearer terms for data use, ambiguity remains about how this data can be reused for AI purposes after the trial ends. There are very few or no laws on the use of AI especially in developing countries. Also, there are a lot of misconceptions on the global use of AI. Statement of Objectives: Artificial intelligence as an innovative technology has contributed to a shift in paradigm in conducting clinical research. Unfortunately, AI faces ethical, and regulatory challenges especially in limited resource countries where the technology is still to be consolidated. One of the main concerns of AI involves data re-identification, in which anonymized data can potentially be traced back to individuals, especially when linked with other datasets. Data ownership is also a complex and often controversial area within the healthcare sector. AI developers needs to clearly explain the value of data collection to hospitals and cybersecurity teams to ensure that they understand how the data will be secured and used ethically Methodology: The World Health Organization (WHO) recognizes that AI holds great promise for clinical health research and in the practice of medicine, biomedical and pharmaceutical sciences. WHO also recognizes that, to fully maximize the contribution of AI, there is the need to address the ethical, legal and regulatory challenges for the health care systems, practitioners and beneficiaries of medical and public health services. In this study we have pulled data from accessible websites, peered reviewed open-access publications that deal with the ethical and regulatory concerns of AI, that we have discussed in this writeup. We have attempted to place our focus on the development of AI and applications with particular bias in the ethical and regulatory concerns. We have discussed and given an insight on whether AI can advance the interests of patients and communities within the framework of collective effort to design and implement ethically defensible laws and policies and ethically designed AI technologies. Finally, we have investigated the potential serious negative consequences of ethical principles and human rights obligations if they are not prioritized by those who fund, design, regulate or use AI technologies for health research. Results: From our data mining and access to multiple documentations, vital information has been pooled together by a systematic online search to show that AI is contributing significantly in the growth of global clinical research and advancement of medicine. However, we observed many ethical and regulatory challenges that has impacted health research in developing economies. Ethical challenges include AI and human rights, patient’s privacy, safety and liability, informed consent and data ownership, bias and fairness. For the legal and regulatory challenges, we observed issues with data security compliance, data monitoring and maintenance, transparency and accountability, data collection, data storage and use. The role of third-party vendors in AI healthcare solutions and finally AI development and integration into the health systems has also been reviewed. Conclusion: The advancement of AI, coupled with the innovative digital health technology has made a significant contribution to address some challenges in clinical research, within the domain of medicine, biomedical and pharmaceutical products development. Despite the challenging ethical and regulatory challenges AI has impacted significant innovation and technology in clinical research, especially within the domain of drug discovery and development, and clinical trials studies.
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Göktepe-Kavis, Pinar, Florence M. Aellen, Sigurd L. Alnes, and Athina Tzovara. "Sleep Research in the Era of AI." Clinical and Translational Neuroscience 8, no. 1 (2024): 13. http://dx.doi.org/10.3390/ctn8010013.
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The field of sleep research is both broad and rapidly evolving. It spans from the diagnosis of sleep-related disorders to investigations of how sleep supports memory consolidation. The study of sleep includes a variety of approaches, starting with the sole focus on the visual interpretation of polysomnography characteristics and extending to the emergent use of advanced signal processing tools. Insights gained using artificial intelligence (AI) are rapidly reshaping the understanding of sleep-related disorders, enabling new approaches to basic neuroscientific studies. In this opinion article, we explore the emergent role of AI in sleep research, along two different axes: one clinical and one fundamental. In clinical research, we emphasize the use of AI for automated sleep scoring, diagnosing sleep-wake disorders and assessing measurements from wearable devices. In fundamental research, we highlight the use of AI to better understand the functional role of sleep in consolidating memories. While AI is likely to facilitate new advances in the field of sleep research, we also address challenges, such as bridging the gap between AI innovation and the clinic and mitigating inherent biases in AI models. AI has already contributed to major advances in the field of sleep research, and mindful deployment has the potential to enable further progress in the understanding of the neuropsychological benefits and functions of sleep.
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Huespe, Ivan A., Jorge Echeverri, Aisha Khalid, et al. "Clinical Research With Large Language Models Generated Writing—Clinical Research with AI-assisted Writing (CRAW) Study." Critical Care Explorations 5, no. 10 (2023): e0975. http://dx.doi.org/10.1097/cce.0000000000000975.
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IMPORTANCE: The scientific community debates Generative Pre-trained Transformer (GPT)-3.5’s article quality, authorship merit, originality, and ethical use in scientific writing. OBJECTIVES: Assess GPT-3.5’s ability to craft the background section of critical care clinical research questions compared to medical researchers with H-indices of 22 and 13. DESIGN: Observational cross-sectional study. SETTING: Researchers from 20 countries from six continents evaluated the backgrounds. PARTICIPANTS: Researchers with a Scopus index greater than 1 were included. MAIN OUTCOMES AND MEASURES: In this study, we generated a background section of a critical care clinical research question on “acute kidney injury in sepsis” using three different methods: researcher with H-index greater than 20, researcher with H-index greater than 10, and GPT-3.5. The three background sections were presented in a blinded survey to researchers with an H-index range between 1 and 96. First, the researchers evaluated the main components of the background using a 5-point Likert scale. Second, they were asked to identify which background was written by humans only or with large language model-generated tools. RESULTS: A total of 80 researchers completed the survey. The median H-index was 3 (interquartile range, 1–7.25) and most (36%) researchers were from the Critical Care specialty. When compared with researchers with an H-index of 22 and 13, GPT-3.5 was marked high on the Likert scale ranking on main background components (median 4.5 vs. 3.82 vs. 3.6 vs. 4.5, respectively; p < 0.001). The sensitivity and specificity to detect researchers writing versus GPT-3.5 writing were poor, 22.4% and 57.6%, respectively. CONCLUSIONS AND RELEVANCE: GPT-3.5 could create background research content indistinguishable from the writing of a medical researcher. It was marked higher compared with medical researchers with an H-index of 22 and 13 in writing the background section of a critical care clinical research question.
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Shamszare, Hamid, and Avishek Choudhury. "Clinicians’ Perceptions of Artificial Intelligence: Focus on Workload, Risk, Trust, Clinical Decision Making, and Clinical Integration." Healthcare 11, no. 16 (2023): 2308. http://dx.doi.org/10.3390/healthcare11162308.
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Artificial intelligence (AI) offers the potential to revolutionize healthcare, from improving diagnoses to patient safety. However, many healthcare practitioners are hesitant to adopt AI technologies fully. To understand why, this research explored clinicians’ views on AI, especially their level of trust, their concerns about potential risks, and how they believe AI might affect their day-to-day workload. We surveyed 265 healthcare professionals from various specialties in the U.S. The survey aimed to understand their perceptions and any concerns they might have about AI in their clinical practice. We further examined how these perceptions might align with three hypothetical approaches to integrating AI into healthcare: no integration, sequential (step-by-step) integration, and parallel (side-by-side with current practices) integration. The results reveal that clinicians who view AI as a workload reducer are more inclined to trust it and are more likely to use it in clinical decision making. However, those perceiving higher risks with AI are less inclined to adopt it in decision making. While the role of clinical experience was found to be statistically insignificant in influencing trust in AI and AI-driven decision making, further research might explore other potential moderating variables, such as technical aptitude, previous exposure to AI, or the specific medical specialty of the clinician. By evaluating three hypothetical scenarios of AI integration in healthcare, our study elucidates the potential pitfalls of sequential AI integration and the comparative advantages of parallel integration. In conclusion, this study underscores the necessity of strategic AI integration into healthcare. AI should be perceived as a supportive tool rather than an intrusive entity, augmenting the clinicians’ skills and facilitating their workflow rather than disrupting it. As we move towards an increasingly digitized future in healthcare, comprehending the among AI technology, clinician perception, trust, and decision making is fundamental.
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Yoo, Dong Whi, Hayoung Woo, Sachin R. Pendse, et al. "Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024): 1–24. http://dx.doi.org/10.1145/3637372.
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In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.
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Li, Ping, Manyi Zhuang, Jun Tang, and Jian Huang. "Research on Automatic Recognition and Auxiliary Diagnosis of Artificial Intelligence in Skin Diseases." Frontiers in Computing and Intelligent Systems 11, no. 3 (2025): 112–26. https://doi.org/10.54097/a0askn96.
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Artificial intelligence (AI) has made significant strides in skin disease diagnosis, demonstrating impressive potential in the classification and detection of conditions such as skin cancer. However, several challenges remain that hinder the full clinical adoption of AI-driven diagnostic systems. This paper reviews the current advancements and key research areas aimed at optimizing AI models for dermatological applications. Key areas for future research include the development of more advanced deep learning architectures, such as multimodal fusion methods, which integrate dermoscopic images with structured clinical data for more comprehensive diagnostics. Additionally, there is a growing emphasis on creating personalized AI models that account for individual patient characteristics, such as age, gender, and genetics, to improve diagnostic accuracy. The integration of explainable AI (XAI) techniques, auxiliary tasks like lesion segmentation, and multi-center clinical research is essential to ensure transparency, trust, and generalizability across diverse populations. Moreover, ethical concerns related to bias, data privacy, and accountability must be addressed to ensure fairness and transparency in AI systems. This review highlights the importance of interdisciplinary collaboration and proposes future directions to enhance the reliability, scalability, and clinical applicability of AI-driven skin disease diagnosis, ultimately improving patient outcomes and accessibility to care.
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Corsello, Antonio, and Andrea Santangelo. "May Artificial Intelligence Influence Future Pediatric Research?—The Case of ChatGPT." Children 10, no. 4 (2023): 757. http://dx.doi.org/10.3390/children10040757.
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Background: In recent months, there has been growing interest in the potential of artificial intelligence (AI) to revolutionize various aspects of medicine, including research, education, and clinical practice. ChatGPT represents a leading AI language model, with possible unpredictable effects on the quality of future medical research, including clinical decision-making, medical education, drug development, and better research outcomes. Aim and Methods: In this interview with ChatGPT, we explore the potential impact of AI on future pediatric research. Our discussion covers a range of topics, including the potential positive effects of AI, such as improved clinical decision-making, enhanced medical education, faster drug development, and better research outcomes. We also examine potential negative effects, such as bias and fairness concerns, safety and security issues, overreliance on technology, and ethical considerations. Conclusions: While AI continues to advance, it is crucial to remain vigilant about the possible risks and limitations of these technologies and to consider the implications of these technologies and their use in the medical field. The development of AI language models represents a significant advancement in the field of artificial intelligence and has the potential to revolutionize daily clinical practice in every branch of medicine, both surgical and clinical. Ethical and social implications must also be considered to ensure that these technologies are used in a responsible and beneficial manner.
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Aditya, Gadiko. "The Synergy of AI and Human Expertise in Clinical Research: A Path to Optimized Medical Reviews." Journal of Scientific and Engineering Research 10, no. 12 (2023): 173–79. https://doi.org/10.5281/zenodo.11221029.
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The integration of Artificial Intelligence (AI) with human expertise in clinical research promises a revolutionary shift in medical reviews, enhancing efficiency, data integrity, and patient safety. This paper explores the synergy between AI and human expertise, focusing on optimizing medical re- view processes in clinical trials. We highlight the challenges of increasing data volume and complexity, increasing complexity of data issues, pattern recognition, and data analysis, including multimodal data integration. Key to our discourse is the col- laborative model between AI systems and medical professionals. We argue that human judgment and AI’s analytical prowess are complementary, offering a balanced approach to Medical data review. This synergy, supported by continuous feedback loops for AI refinement, ensures the precision and reliability of clinical trial outcomes. Ethical considerations, particularly regarding data accuracy, patient privacy, and bias, are addressed by recommending transparency, human oversight, and clear operational guidelines. We conclude that the future of clinical trials hinges on the effective partnership between AI and human expertise, significantly advancing clinical research goals. This paper presents a concise blueprint for leveraging AI in clinical trials, underscoring its potential to redefine medical review standards and improve healthcare outcomes.
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Julian Y.V, Borges. "Artificial Intelligence in Pain Management: Advancing Translational Science in Digital Health Research from Bench to Bedside." Advances in Machine Learning & Artificial Intelligence 5, no. 3 (2024): 01–06. http://dx.doi.org/10.33140/amlai.05.03.04.
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Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This chapter delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated. Through the use of predictive modeling, real-time monitoring, and personalized treatment plans, AI significantly enhances the precision, efficiency, and effectiveness of pain management strategies. The discussion extends to various AI applications, shedding light on the ethical considerations that accompany these technological advancements, as well as outlining future research directions. Collectively, these insights underscore the immense potential of AI to not only improve pain management practices but also to significantly elevate patient outcomes. Central to this transformation is the role of translational science in bridging the gap between theoretical AI models and their practical, clinical applications. This "bench to bedside" approach ensures that innovations in AI are not merely confined to research environments but are actively integrated into real-world patient care. For instance, AI-powered predictive analytics in pain management, driven by sophisticated machine learning algorithms, have progressed from computational experiments to clinical trials, and ultimately, to widespread implementation in healthcare settings. These AI models are now being utilized in hospitals to assess patient pain levels in real-time, predict opioid requirements, and optimize pain management protocols. This progression exemplifies how translational science is facilitating a paradigm shift in healthcare, positioning AI as an indispensable tool in modern pain management.
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Balti, Ali Asgar, Mohammad Hassanain, Mohd Ajaz, and Williyat Ali. "Impact of generative AI on biochemical research and clinical practices." International Journal of Clinical Biochemistry and Research 12, no. 1 (2025): 68–70. https://doi.org/10.18231/j.ijcbr.2025.010.
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Burstein, Harold J., Ann Alexis Prestrud, Jerome Seidenfeld, et al. "American Society of Clinical Oncology Clinical Practice Guideline: Update on Adjuvant Endocrine Therapy for Women With Hormone Receptor–Positive Breast Cancer." Journal of Clinical Oncology 28, no. 23 (2010): 3784–96. http://dx.doi.org/10.1200/jco.2009.26.3756.
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PurposeTo develop evidence-based guidelines, based on a systematic review, for endocrine therapy for postmenopausal women with hormone receptor–positive breast cancer.MethodsA literature search identified relevant randomized trials. Databases searched included MEDLINE, PREMEDLINE, the Cochrane Collaboration Library, and those for the Annual Meetings of the American Society of Clinical Oncology (ASCO) and the San Antonio Breast Cancer Symposium (SABCS). The primary outcomes of interest were disease-free survival, overall survival, and time to contralateral breast cancer. Secondary outcomes included adverse events and quality of life. An expert panel reviewed the literature, especially 12 major trials, and developed updated recommendations.ResultsAn adjuvant treatment strategy incorporating an aromatase inhibitor (AI) as primary (initial endocrine therapy), sequential (using both tamoxifen and an AI in either order), or extended (AI after 5 years of tamoxifen) therapy reduces the risk of breast cancer recurrence compared with 5 years of tamoxifen alone. Data suggest that including an AI as primary monotherapy or as sequential treatment after 2 to 3 years of tamoxifen yields similar outcomes. Tamoxifen and AIs differ in their adverse effect profiles, and these differences may inform treatment preferences.ConclusionThe Update Committee recommends that postmenopausal women with hormone receptor–positive breast cancer consider incorporating AI therapy at some point during adjuvant treatment, either as up-front therapy or as sequential treatment after tamoxifen. The optimal timing and duration of endocrine treatment remain unresolved. The Update Committee supports careful consideration of adverse effect profiles and patient preferences in deciding whether and when to incorporate AI therapy.
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Srivastava, Ankita, Marco Marabelli, Danielle Blanch-Hartigan, et al. "The Present and Future of AI: Ethical Issues and Research Opportunities." Communications of the Association for Information Systems 56 (2025): 255–73. https://doi.org/10.17705/1cais.05611.
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Healthcare is currently a fast-changing industry with AI and generative AI (GenAI) playing a prominent role in the transformation of clinical as well as managerial practices. Clinical practices involve AI to diagnose diseases and develop new drugs and compounds, while managerial practices concern AI-supporting processes such as billing patients and insurance companies, handling electronic medical records, and supporting remote connections with patients, increasingly using virtual and augmented reality. Yet, all these opportunities offered by AI come with challenges involving potential ethical issues, such as discrimination, bias, lack of accessibility, and privacy issues. In March 2024, we organized a panel with healthcare experts, attended by 48 academics and practitioners, and discussed the innovative power of AI, along with its ethical concerns and in doing so, we attempted to address some of these concerns. This panel report summarizes key findings and outlines a research agenda for IS scholars engaged in health-IT research specifically examining the new frontiers of AI and GenAI.
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Peek, Niels, Daniel Capurro, Vlada Rozova, and Sabine N. van der Veer. "Bridging the Gap: Challenges and Strategies for the Implementation of Artificial Intelligence-based Clinical Decision Support Systems in Clinical Practice." Yearbook of Medical Informatics 33, no. 01 (2024): 103–14. https://doi.org/10.1055/s-0044-1800729.
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Summary Objectives: Despite the surge in development of artificial intelligence (AI) algorithms to support clinical decision-making, few of these algorithms are used in practice. We reviewed recent literature on clinical deployment of AI-based clinical decision support systems (AI-CDSS), and assessed the maturity of AI-CDSS implementation research. We also aimed to compare and contrast implementation of rule-based CDSS with implementation of AI-CDSS, and to give recommendations for future research in this area. Methods: We searched PubMed and Scopus for publications in 2022 and 2023 that focused on AI and/or CDSS, health care, and implementation research, and extracted: clinical setting; clinical task; translational research phase; study design; participants; implementation theory, model or framework used; and key findings. Results: We selected and described a total of 31 recent papers addressing implementation of AI-CDSS in clinical practice, categorised into four groups: (i) Implementation theories, frameworks, and models (4 papers); (ii) Stakeholder perspectives (22 papers); (iii) Implementation feasibility (three papers); and (iv) Technical infrastructure (2 papers). Stakeholders saw potential benefits of AI-CDSS, but emphasized the need for a strong evidence base and indicated that systems should fit into clinical workflows. There were clear similarities with rule-based CDSS, but also differences with respect to trust and transparency, knowledge, intellectual property, and regulation. Conclusions: The field of AI-CDSS implementation research is still in its infancy. It can be strengthened by grounding studies in established theories, models and frameworks from implementation science, focusing on the perspectives of stakeholder groups other than healthcare professionals, conducting more real-world implementation feasibility studies, and through development of reusable technical infrastructure that facilitates rapid deployment of AI-CDSS in clinical practice.
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Athanasopoulou, Konstantina, Glykeria N. Daneva, Panagiotis G. Adamopoulos, and Andreas Scorilas. "Artificial Intelligence: The Milestone in Modern Biomedical Research." BioMedInformatics 2, no. 4 (2022): 727–44. http://dx.doi.org/10.3390/biomedinformatics2040049.
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In recent years, the advent of new experimental methodologies for studying the high complexity of the human genome and proteome has led to the generation of an increasing amount of digital information, hence bioinformatics, which harnesses computer science, biology, and chemistry, playing a mandatory role for the analysis of the produced datasets. The emerging technology of Artificial Intelligence (AI), including Machine Learning (ML) and Artificial Neural Networks (ANNs), is nowadays at the core of biomedical research and has already paved the way for significant breakthroughs in both biological and medical sciences. AI and computer science have transformed traditional medicine into modern biomedicine, thus promising a new era in systems biology that will enhance drug discovery strategies and facilitate clinical practice. The current review defines the main categories of AI and thoroughly describes the fundamental principles of the widely used ML, ANNs and DL approaches. Furthermore, we aim to underline the determinant role of AI-based methods in various biological research fields, such as proteomics and drug design techniques, and finally, investigate the implication of AI in everyday clinical practice and healthcare systems. Finally, this review also highlights the challenges and future directions of AI in Modern Biomedical study.
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Friedman, Emma, Matthew John Baumann, Shruti Sehgal, et al. "Pragmatic Research and Clinical Duties: Solutions Through Precision AI-Enabled Clinically Embedded Research." American Journal of Bioethics 23, no. 8 (2023): 50–52. http://dx.doi.org/10.1080/15265161.2023.2217126.
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Sak, Jarosław, and Magdalena Suchodolska. "Artificial Intelligence in Nutrients Science Research: A Review." Nutrients 13, no. 2 (2021): 322. http://dx.doi.org/10.3390/nu13020322.
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Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.
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Kalbhor, Mr Chaitanya Sunil. "Aritifical Intelligence in Clinical Trials." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2470–77. https://doi.org/10.22214/ijraset.2025.68764.
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Abstract: By improving the effectiveness, precision, and flexibility of clinical research procedures, artificial intelligence (AI) is quickly changing the clinical trial environment. AI provides creative answers to many of the conventional problems encountered in clinical trials, ranging from intelligent patient recruiting and protocol design to real-time data analysis and safety monitoring. Improved patient matching, quicker decision-making, and better trial results have all been made possible by recent developments in machine learning, natural language processing, and predictive analytics. Personalized treatment plans, decentralized trial models, and quicker drug development are all possible with the use of AI technology into clinical trials as they advance further. The main uses, current developments, and anticipated future developments of AI in clinical trials are highlighted in this paper, highlighting the technology's potential to transform clinical research and enhance patient care
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Rugo, Hope, Xianchen Liu, Benjamin Li, et al. "Abstract P3-01-15: Real-world effectiveness of palbociclib plus aromatase inhibitors (AI) in African American (AA) patients with metastatic breast cancer (MBC)." Cancer Research 83, no. 5_Supplement (2023): P3–01–15—P3–01–15. http://dx.doi.org/10.1158/1538-7445.sabcs22-p3-01-15.
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Abstract Background: Randomized clinical trials and a growing body of real-world evidence have demonstrated clinical benefit of cyclin dependent kinase 4/6 inhibitors (CDK4/6i) in combination with endocrine therapy for HR+/HER2- (MBC). In the National Comprehensive Cancer Network (NCCN) guidelines, CDK4/6i + AI or Fulvestrant is recognized as a preferred regimen for HR+/HER2- MBC. Disparities in survival and clinical outcomes between AA and white breast cancer patients are well documented, but AA patients were not well represented in CDK4/6i randomized clinical trials. We compared real-world progression free survival (rwPFS) and overall survival (OS) of palbociclib plus AI (PB+AI) vs AI alone in AA patients with HR+/HER2- MBC in US clinical practices. Methods: The Flatiron Health longitudinal database contains electronic health records from &gt;280 cancer clinics representing &gt; 2.4 million actively treated cancer patients in the US. We conducted a retrospective analysis of 270 AA patients from the Flatiron database with HR+/HER2- MBC who started PB+AI or AI as first-line therapy between February 2015 and March 2020, Patients were evaluated from start of PB+AI or AI to September 30, 2020 (Data cutoff date), death, or last visit, whichever came first. OS was defined as months from start of PB+AI or AI to death. Patients were censored at the end of the study if they were living. rwPFS was defined as months from start of PB+AI or AI to death or disease progression, evaluated based on clinical assessment or radiographic scan/tissue biopsy. Cox proportional-hazards models were used to estimate the relative effectiveness of PB+AI vs AI without and with adjustment of baseline demographics and clinical characteristics. Results: Of the 270 eligible patients, 127 (47.0%) were treated with PB+AI and 143 (53.0%) were treated with AI. Median age was 64.0 years in PB+AI patients and 68.0 years in AI patients, respectively. Median follow-up was 24.0 months for PB+AI and 18.2 months for AI treated patients. Compared with AI patients, those treated with PB+AI were more likely to have de novo MBC (48.6% vs 30.8%) and to have ≥2 metastatic sites (41.7% vs 29.4%). Of the PB+AI patients, 82.7% started PB at 125mg/day and 30.7% experienced dose adjustment. Median OS was not reached (NR, 95%CI=(38.2-NR)) in PB+AI patients vs 28.2 months (95%CI=19.2-52.8) in AI patients (HR=0.46, 95%CI=0.31-0.68, p =&lt; 0.001; adjusted HR=0.56, 95%CI=0.36-0.89, p=0.013). Median rwPFS was 18.0 months (95%CI = 12.4 – 26.7) in PB+AI patients and 10.5 months (95%CI=7.0-13.4) in AI patients (HR=0.63, 95%CI=0.44-0.88, p &lt; 0.007; Adjusted HR=0.74, 95%CI=0.47-1.17, p =0.199). Conclusions: This comparative analysis of palbociclib plus AI vs AI alone provides evidence that first-line palbociclib in combination with endocrine is associated with improved effectiveness for AA patients with HR+/HER2- MBC in the real-world setting. Additonal studies with larger cohorts are needed to provide additional evidence of outcomes and safety for AA patients in routine clinical practice. Table. Patient characteristics and effectiveness outcomes Citation Format: Hope Rugo, Xianchen Liu, Benjamin Li, Lynn McRoy, Connie Chen, Rachel M. Layman, Adam M. Brufsky. Real-world effectiveness of palbociclib plus aromatase inhibitors (AI) in African American (AA) patients with metastatic breast cancer (MBC) [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-01-15.
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D, Nandhini. "AI DERMATOLOGY." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46443.
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Abstract - This research investigates the possible change which can be brought about by the application of Artificial Intelligence (AI) in Dermatology with particular emphasis on skin disease prediction. In the face of the rising problem of skin diseases and their early identification and correct diagnosis, the study discusses the rising role of AI-based applications that use deep learning and a vast amount of skin disease images for their identification and prediction. Conventional dermatology depends a lot on the clinical examination and the doctor’s diagnosis which is often subjective and imprecise. The research focuses on the serious limitations of human cognition in the identification of the basic patterns of skin diseases, which may result in incorrect or late diagnosis. In conclusion, the exploration is on how these AI tools are efficient and accurate in predicting skin diseases and how such tools can be used to reduce diagnostic errors, improve patients’ care, and increase the number of people with access to dermatological care. This research advocates for a seamless integration of AI-driven dermatology applications into clinical practice, presenting a holistic approach that enhances diagnostic precision and supports healthcare professionals in making informed decisions, all while improving the overall patient experience in an increasingly digital healthcare landscape. . Key Words: AI, Skin Disease Prediction, CNN
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Sáez, Carlos, Pablo Ferri, and Juan M. García-Gómez. "Resilient Artificial Intelligence in Health: Synthesis and Research Agenda Toward Next-Generation Trustworthy Clinical Decision Support." Journal of Medical Internet Research 26 (June 28, 2024): e50295. http://dx.doi.org/10.2196/50295.
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Artificial intelligence (AI)–based clinical decision support systems are gaining momentum by relying on a greater volume and variety of secondary use data. However, the uncertainty, variability, and biases in real-world data environments still pose significant challenges to the development of health AI, its routine clinical use, and its regulatory frameworks. Health AI should be resilient against real-world environments throughout its lifecycle, including the training and prediction phases and maintenance during production, and health AI regulations should evolve accordingly. Data quality issues, variability over time or across sites, information uncertainty, human-computer interaction, and fundamental rights assurance are among the most relevant challenges. If health AI is not designed resiliently with regard to these real-world data effects, potentially biased data-driven medical decisions can risk the safety and fundamental rights of millions of people. In this viewpoint, we review the challenges, requirements, and methods for resilient AI in health and provide a research framework to improve the trustworthiness of next-generation AI-based clinical decision support.
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Gnanasambandam, Anjali. "Artificial Intelligence in Clinical Trials- Future Prospectives." Bioequivalence & Bioavailability International Journal 7, no. 1 (2023): 1–8. http://dx.doi.org/10.23880/beba-16000196.
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Clinical trials are essential for delivering novel medications, technology, and procedures to the market and clinical practice. Only 10% of these studies complete the entire procedure from the drug design to the four phases of development, because clinical trials are becoming more expensive and difficult to perform. The population's health, standard treatment, health economics, and sustainability suffered greatly from this low completion rate. Artificial intelligence (AI) is one of the tools that could streamline some of the processes which are the most tedious operations, like patient selection, matching, and enrollment; better patient selection could also minimize harmful treatment and its side effects. The widespread implementation of AI technology in clinical trials still faces many challenges and requires more high-quality prospective clinical validation. In this review, we discussed the prospective applications of AI in clinical research and patient care in the future
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Sequí-Sabater, José Miguel, and Diego Benavent. "Artificial intelligence in rheumatology research: what is it good for?" RMD Open 11, no. 1 (2025): e004309. https://doi.org/10.1136/rmdopen-2024-004309.
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Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support. This review explores the current applications and future potential of both discriminative and generative AI in rheumatology. It also highlights the challenges posed by these technologies, such as ethical concerns and the need for rigorous validation and regulatory oversight. The integration of AI in rheumatology promises substantial advancements but requires a balanced approach to optimise benefits and minimise potential possible downsides.
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Regina, Odette Agnes. "Artificial Intelligence in Diabetes Care: Transforming Diagnosis, Management, and Research- A Mini Review." IDOSR JOURNAL OF COMPUTER AND APPLIED SCIENCES 9, no. 1 (2024): 11–14. http://dx.doi.org/10.59298/jcas/2024/91.1114000.
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Artificial intelligence (AI) is revolutionizing diabetes care by transforming the landscape of diagnosis, management, and research. This review explores the diverse applications of AI in diabetes, including predictive modeling, personalized treatment strategies, clinical decision support systems, and drug discovery. The integration of AI with advanced data analytics, machine learning algorithms, and big data has enabled more accurate risk prediction, early disease detection, and optimized therapeutic interventions. Challenges such as data privacy, algorithm transparency, and clinical validation are also discussed. Overall, AI holds immense promise in reshaping the future of diabetes care, enhancing patient outcomes, and advancing scientific understanding. The existing literature on the involvement of AI in diabetes mellitus care is summarised in this review. A thorough search of the literature was done with databases such as PubMed, Google Scholar, and Web of Science. Keywords: Artificial intelligence, AI, Diabetes mellitus, Personalized medicine
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Thai, Kelly, Kate H. Tsiandoulas, Elizabeth A. Stephenson, et al. "Perspectives of Youths on the Ethical Use of Artificial Intelligence in Health Care Research and Clinical Care." JAMA Network Open 6, no. 5 (2023): e2310659. http://dx.doi.org/10.1001/jamanetworkopen.2023.10659.
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ImportanceUnderstanding the views and values of patients is of substantial importance to developing the ethical parameters of artificial intelligence (AI) use in medicine. Thus far, there is limited study on the views of children and youths. Their perspectives contribute meaningfully to the integration of AI in medicine.ObjectiveTo explore the moral attitudes and views of children and youths regarding research and clinical care involving health AI at the point of care.Design, Setting, and ParticipantsThis qualitative study recruited participants younger than 18 years during a 1-year period (October 2021 to March 2022) at a large urban pediatric hospital. A total of 44 individuals who were receiving or had previously received care at a hospital or rehabilitation clinic contacted the research team, but 15 were found to be ineligible. Of the 29 who consented to participate, 1 was lost to follow-up, resulting in 28 participants who completed the interview.ExposuresParticipants were interviewed using vignettes on 3 main themes: (1) health data research, (2) clinical AI trials, and (3) clinical use of AI.Main Outcomes and MeasuresThematic description of values surrounding health data research, interventional AI research, and clinical use of AI.ResultsThe 28 participants included 6 children (ages, 10-12 years) and 22 youths (ages, 13-17 years) (16 female, 10 male, and 3 trans/nonbinary/gender diverse). Mean (SD) age was 15 (2) years. Participants were highly engaged and quite knowledgeable about AI. They expressed a positive view of research intended to help others and had strong feelings about the uses of their health data for AI. Participants expressed appreciation for the vulnerability of potential participants in interventional AI trials and reinforced the importance of respect for their preferences regardless of their decisional capacity. A strong theme for the prospective use of clinical AI was the desire to maintain bedside interaction between the patient and their physician.Conclusions and RelevanceIn this study, children and youths reported generally positive views of AI, expressing strong interest and advocacy for their involvement in AI research and inclusion of their voices for shared decision-making with AI in clinical care. These findings suggest the need for more engagement of children and youths in health care AI research and integration.
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Azmi, Sarfuddin, Faisal Kunnathodi, Haifa F. Alotaibi, et al. "Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review." Diagnostics 15, no. 3 (2025): 396. https://doi.org/10.3390/diagnostics15030396.
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Purpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, personalize treatments, and improve healthcare interventions for obesity. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, with keywords including “artificial intelligence”, “machine learning”, “deep learning”, “obesity”, “obesity management”, and related terms. Studies focusing on AI’s role in obesity research, management, and therapeutic interventions were reviewed, including observational studies, systematic reviews, and clinical applications. Results: This review identifies numerous AI-driven models, such as ML and DL, used in obesity prediction, patient stratification, and personalized management strategies. Applications of AI in obesity research include risk prediction, early detection, and individualization of treatment plans. AI has facilitated the development of predictive models utilizing various data sources, such as genetic, epigenetic, and clinical data. However, AI models vary in effectiveness, influenced by dataset type, research goals, and model interpretability. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated to optimize model selection. Conclusions: AI offers promising advancements in obesity management, enabling more personalized and efficient care. While technology presents considerable potential, challenges such as data quality, ethical considerations, and technical requirements remain. Addressing these will be essential to fully harness AI’s potential in obesity research and treatment, supporting a shift toward precision healthcare.
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Wu, Chieh-Chen, Md Mohaimenul Islam, Tahmina Nasrin Poly, and Yung-Ching Weng. "Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research." Diagnostics 14, no. 4 (2024): 397. http://dx.doi.org/10.3390/diagnostics14040397.
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Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.
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Bikash Medhi, Dr. Himanshu Sharma, Dr. Tamanna Kaundal, and Dr. Ajay Prakash. "Artificial Intelligence: A Catalyst for Breakthroughs in Nanotechnology and Pharmaceutical Research." International Journal of Pharmaceutical Sciences and Nanotechnology(IJPSN) 17, no. 4 (2024): 7439–45. http://dx.doi.org/10.37285/ijpsn.2024.17.4.1.
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Artificial intelligence (AI) is revolutionizing nanotechnology and pharmaceutical research by streamlining drug discovery, optimizing formulations, and personalizing treatments through predictive modelling and data analysis. Without AI, the pharmaceutical industry requires more time due to less effective drug discovery, inefficient clinical trials, and prolonged regulatory processes, resulting in higher costs and delayed treatments1. The integration of AI with nanotechnology and pharmaceutical science is revolutionizing medicine, opening up new possibilities for diagnosis, treatment, and personalized healthcare. It also enhances clinical treatments and identifies new uses for existing drugs, reducing development time and costs2. By leveraging machine learning algorithms, researchers can predict the properties and behaviour of nanomaterials, facilitating the development of nanoparticles that can deliver drugs more efficiently to specific cells or tissues3. AI accelerates nano product development by optimizing nanomaterial design, predicting nanoparticle toxicity, and enhancing nanomedicine formulation. For example, AI has been used to design nanoparticles for targeted drug delivery, improving their efficiency and safety4. AI-enabled nanotechnology can enhance molecular profiling and early diagnosis, refine the design of nanomedicines, and improve their efficacy. By optimizing nanomedicine properties, achieving effective drug synergy, and reducing nanotoxicity, AI facilitates better targetability and accelerates the development of personalized treatments.
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Johnston, Stephen R. D., Roberto Hegg, Seock-Ah Im, et al. "Phase III, Randomized Study of Dual Human Epidermal Growth Factor Receptor 2 (HER2) Blockade With Lapatinib Plus Trastuzumab in Combination With an Aromatase Inhibitor in Postmenopausal Women With HER2-Positive, Hormone Receptor–Positive Metastatic Breast Cancer: ALTERNATIVE." Journal of Clinical Oncology 36, no. 8 (2018): 741–48. http://dx.doi.org/10.1200/jco.2017.74.7824.
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Purpose Human epidermal growth factor receptor 2 (HER2) targeting plus endocrine therapy (ET) improved clinical benefit in HER2-positive, hormone receptor (HR)–positive metastatic breast cancer (MBC) versus ET alone. Dual HER2 blockade enhances clinical benefit versus single HER2 blockade. The ALTERNATIVE study evaluated the efficacy and safety of dual HER2 blockade plus aromatase inhibitor (AI) in postmenopausal women with HER2-positive/HR-positive MBC who received prior ET and prior neo(adjuvant)/first-line trastuzumab (TRAS) plus chemotherapy. Methods Patients were randomly assigned (1:1:1) to receive lapatinib (LAP) + TRAS + AI, TRAS + AI, or LAP + AI. Patients for whom chemotherapy was intended were excluded. The primary end point was progression-free survival (PFS; investigator assessed) with LAP + TRAS + AI versus TRAS + AI. Secondary end points were PFS (comparison of other arms), overall survival, overall response rate, clinical benefit rate, and safety. Results Three hundred fifty-five patients were included in this analysis: LAP + TRAS + AI (n = 120), TRAS + AI (n = 117), and LAP + AI (n = 118). Baseline characteristics were balanced. The study met its primary end point; superior PFS was observed with LAP + TRAS + AI versus TRAS + AI (median PFS, 11 v 5.7 months; hazard ratio, 0.62; 95% CI, 0.45 to 0.88; P = .0064). Consistent PFS benefit was observed in predefined subgroups. Overall response rate, clinical benefit rate, and overall survival also favored LAP + TRAS + AI. The median PFS with LAP + AI versus TRAS + AI was 8.3 versus 5.7 months (hazard ratio, 0.71; 95% CI, 0.51 to 0.98; P = .0361). Common adverse events (AEs; ≥ 15%) with LAP + TRAS + AI, TRAS + AI, and LAP + AI were diarrhea (69%, 9%, and 51%, respectively), rash (36%, 2%, and 28%, respectively), nausea (22%, 9%, and 22%, respectively), and paronychia (30%, 0%, and 15%, respectively), mostly grade 1 or 2. Serious AEs were reported similarly across the three groups, and AEs leading to discontinuation were lower with LAP + TRAS + AI. Conclusion Dual HER2 blockade with LAP + TRAS + AI showed superior PFS benefit versus TRAS + AI in patients with HER2-positive/HR-positive MBC. This combination offers an effective and safe chemotherapy-sparing alternative treatment regimen for this patient population.
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Diaconu, A., F. D. Cojocaru, I. Gardikiotis, et al. "Expending the power of artificial intelligence in preclinical research: an overview." IOP Conference Series: Materials Science and Engineering 1254, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1757-899x/1254/1/012036.
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Abstract Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs, interpret and learn from such feedbacks, and display related and flexible independent actions that help the entity reach a specific aim over a period of time. By extending its health-care applications continuously, the ultimate AI target is to use machine simulation of human intelligence processes such as learning, reasoning, and self-correction, to mimic human behaviour. AI is extensively used in diverse sectors of medicine, including clinical trials, drug discovery and development, understanding of target-disease associations, disease prediction, imaging, and precision medicine. In this review, we firstly describe the limitations and challenges of the AI tools and techniques utilized in medicine, followed by current uses and applications of AI in the translational field, highlighting the cardio-renal preclinical models with potential to contribute to future clinical research.