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Journal articles on the topic 'Chatbot for Healthcare'
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Inupakutika, D., M. Nadim, G. R. Gunnam, S. Kaghyan, D. Akopian, P. Chalela, and A. G. Ramirez. "Integration of NLP and Speech-to-text Applications with Chatbots." Electronic Imaging 2021, no. 3 (June 18, 2021): 35–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.3.mobmu-035.
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With the evolving artificial intelligence technology, the chatbots are becoming smarter and faster lately. Chatbots are typically available round the clock providing continuous support and services. A chatbot or a conversational agent is a program or software that can communicate using natural language with humans. The challenge of developing an intelligent chatbot still exists ever since the onset of artificial intelligence. The functionality of chatbots can range from business oriented short conversations to healthcare intervention based longer conversations. However, the primary role that the chatbots have to play is in understanding human utterances in order to respond appropriately. To that end, there is an increased emergence of Natural Language Understanding (NLU) engines by popular cloud service providers. The NLU services identify entities and intents from the user utterances provided as input. Thus, in order to integrate such understanding to a chatbot, this paper presents a study on existing major NLU platforms. Then, we present a case study chatbot integrated with Google DialogFlow and IBM Watson NLU services and discuss their intent recognition performance.
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Oguntosin, Victoria, and Ayobami Olomo. "Development of an E-Commerce Chatbot for a University Shopping Mall." Applied Computational Intelligence and Soft Computing 2021 (March 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/6630326.
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Chatbots have been used in many fields ranging from education to healthcare and are also used in e-commerce settings. This research aims at developing a web-based chatbot called Hebron for the Covenant University Community Mall. The chatbot is developed using Python and React.js as the programming languages and MySQL (Structured Query Language) server as the database to give a structure to the e-commerce datasets and Admin Portal process. The e-commerce chatbot application for Covenant University Shopping Mall (CUSM) seeks to provide an easy, smart, and comfortable shopping experience for the Covenant University Community.
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Ye, Byeong Jin, Ju Young Kim, Chunhui Suh, Seong Pil Choi, Maro Choi, Dong Hyun Kim, and Byung Chul Son. "Development of a Chatbot Program for Follow-Up Management of Workers’ General Health Examinations in Korea: A Pilot Study." International Journal of Environmental Research and Public Health 18, no. 4 (February 23, 2021): 2170. http://dx.doi.org/10.3390/ijerph18042170.
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(1) Background: Follow-up management of workers’ general health examination (WGHE) is important, but it is not currently well done. Chatbot, a type of digital healthcare tool, is used in various medical fields but has never been developed for follow-up management of WGHE in Korea. (2) Methods: The database containing results and explanations related to WGHE was constructed. Then, the channel, which connects users with the database was created. A user survey regarding effectiveness was administered to 23 healthcare providers. Additionally, interviews on applicability for occupational health services were conducted with six nurses in the agency of occupational health management. (3) Results: Chatbot was implemented on a small scale on the Amazon cloud service (AWS) EC2 using KaKaoTalk and Web Chat as user channels. Regarding the effectiveness, 21 (91.30%) rated the need for chatbots as very high; however, 11 (47.83%) rated the usability as not high. Of the 23 participants, 14 (60.87%) expressed overall satisfaction. Nurses appreciated the chatbot program as a method for resolving accessibility and as an aid for explaining examination results and follow-up management. (4) Conclusions: The effectiveness of WGHE and the applicability in the occupational health service of the chatbot program for follow-up management can be confirmed.
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Sumit. "AI Health Care Chatbot." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 13, 2020): 219–24. http://dx.doi.org/10.46501/ijmtst061241.
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Healthcare bot is a technology that makes interaction between man and machine possible by using Artificial Intelligence with the support of dialog flow. Now a day people tend to seek knowledge or information from internet that concern with health through online healthcare services. To lead a good life healthcare is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The basic aim of this system is to bridge the vocabulary gap between the doctors by giving self-diagnosis from the comfort of one’s place. The proposed idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the medical bot is built. Certain bots act as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a bot only when it can diagnose all kind of disease and provide necessary information. Hence, people will have an idea about their health and have the right protection.
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Pradhan, Rahul, Jaya Shukla, and Mani Bansal. "‘K-Bot’ Knowledge Enabled Personalized Healthcare Chatbot." IOP Conference Series: Materials Science and Engineering 1116, no. 1 (April 1, 2021): 012185. http://dx.doi.org/10.1088/1757-899x/1116/1/012185.
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Nadarzynski, Tom, Oliver Miles, Aimee Cowie, and Damien Ridge. "Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study." DIGITAL HEALTH 5 (January 2019): 205520761987180. http://dx.doi.org/10.1177/2055207619871808.
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Background Artificial intelligence (AI) is increasingly being used in healthcare. Here, AI-based chatbot systems can act as automated conversational agents, capable of promoting health, providing education, and potentially prompting behaviour change. Exploring the motivation to use health chatbots is required to predict uptake; however, few studies to date have explored their acceptability. This research aimed to explore participants’ willingness to engage with AI-led health chatbots. Methods The study incorporated semi-structured interviews (N-29) which informed the development of an online survey (N-216) advertised via social media. Interviews were recorded, transcribed verbatim and analysed thematically. A survey of 24 items explored demographic and attitudinal variables, including acceptability and perceived utility. The quantitative data were analysed using binary regressions with a single categorical predictor. Results Three broad themes: ‘Understanding of chatbots’, ‘AI hesitancy’ and ‘Motivations for health chatbots’ were identified, outlining concerns about accuracy, cyber-security, and the inability of AI-led services to empathise. The survey showed moderate acceptability (67%), correlated negatively with perceived poorer IT skills OR = 0.32 [CI95%:0.13–0.78] and dislike for talking to computers OR = 0.77 [CI95%:0.60–0.99] as well as positively correlated with perceived utility OR = 5.10 [CI95%:3.08–8.43], positive attitude OR = 2.71 [CI95%:1.77–4.16] and perceived trustworthiness OR = 1.92 [CI95%:1.13–3.25]. Conclusion Most internet users would be receptive to using health chatbots, although hesitancy regarding this technology is likely to compromise engagement. Intervention designers focusing on AI-led health chatbots need to employ user-centred and theory-based approaches addressing patients’ concerns and optimising user experience in order to achieve the best uptake and utilisation. Patients’ perspectives, motivation and capabilities need to be taken into account when developing and assessing the effectiveness of health chatbots.
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Gamble, Alyson. "Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective." Aslib Journal of Information Management 72, no. 4 (June 2, 2020): 509–23. http://dx.doi.org/10.1108/ajim-11-2019-0316.
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PurposeFor decades, artificial intelligence (AI) has been utilized within the field of mental healthcare. This paper aims to examine AI chatbots, specifically as offered through mobile applications for mental healthcare (MHapps), with attention to the social implications of these technologies. For example, AI chatbots in MHapps are programmed with therapeutic techniques to assist people with anxiety and depression, but the promise of this technology is tempered by concerns about the apps' efficacy, privacy, safety and security.Design/methodology/approachUtilizing a social informatics perspective, a literature review covering MHapps, with a focus on AI chatbots was conducted from the period of January–April 2019. A borrowed theory approach pairing information science and social work was applied to analyze the literature.FindingsRising needs for mental healthcare, combined with expanding technological developments, indicate continued growth of MHapps and chatbots. While an AI chatbot may provide a person with a place to access tools and a forum to discuss issues, as well as a way to track moods and increase mental health literacy, AI is not a replacement for a therapist or other mental health clinician. Ultimately, if AI chatbots and other MHapps are to have a positive impact, they must be regulated, and society must avoid techno-fundamentalism in relation to AI for mental health.Originality/valueThis study adds to a small but growing body of information science research into the role of AI in the support of mental health.
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Vineeth, R., R. Rithish, D. V. S. N. Sai Varma, and B. V. Ajay Prakash. "Smart Health Care Chatbot for Prognosis of Treatments and Disease Diagnosis Using Machine Learning." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3947–51. http://dx.doi.org/10.1166/jctn.2020.8993.
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In this present world there are various diseases for which treatments and remedies are available abundantly. It is impossible for human to remember all the precautions and remedies to cure the disease. There is no relevant platform that could exhibit all the diseases and their respective remedies. Health professionals are not always available to users on all the time. Hence, the necessity of health care Chatbot plays a major role in this current world. In the proposed idea, we have created a HealthCare Chatbot with Artificial Intelligence techniques which can process the text input and predict the diseases associated with the symptoms given by the user. The HealthCare Chatbot implemented here is a user friendly platform which predicts the probable diseases and the home remedies, we can imply to cure based on the symptoms observed by the user in their knowledge.
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Kamita, Takeshi, Tatsuya Ito, Atsuko Matsumoto, Tsunetsugu Munakata, and Tomoo Inoue. "A Chatbot System for Mental Healthcare Based on SAT Counseling Method." Mobile Information Systems 2019 (March 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/9517321.
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In recent years, mental health management of employees in companies has become increasingly important. As the number of psychotherapists is not enough, it is necessary for employees to be able to keep their mental wellness on their own. A self-guided mental healthcare course using VR devices has been developed, and its stress reduction effect has been validated previously. This study proposes a new version of the course using smartphones and chatbots to enhance its convenience for use and to maintain user motivation for daily and repeated use. The effects of stress reduction and motivation maintenance were acknowledged.
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Avramova-Todorova, Gergana, and Milen Todorov. "Digital technologies for art therapy practices used in healthcare." Medical Science Pulse 13, no. 1 (April 25, 2019): 43–47. http://dx.doi.org/10.5604/01.3001.0013.1604.
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The use of digital technologies influences practically almost all aspects of our daily life. In the field of healthcare, in particular, technology plays a very important in activities related to data collection, data storing, and data analysis. The aim of technology in healthcare is to provide a range of healthcare professionals with access to information that will help increase the cost-effectiveness of care delivery and improve the efficacy of care. Psychology counseling is an area where specific elements, such as evaluation of emotional health, could be supported by the use of appropriate technologies. Such technology could increase accessibility to this type of assistance by reducing lengthy and costly travel to specialized centers. In addition, technology may enable overburdened professionals to increase the reach of their services, and help people with physical limitations who have restricted ability to travel to receive care. So-called ‘virtual assistants’ (also known as ‘chatbots’) could help patients to identify emotional imbalance. In general, the evaluation process could include a series of questions that aim to find the emotional problem, and ultimately to propose a suitable program of art therapy. The current study aims to outline the steps needed to develop a chatbot that is capable of identifying emotional imbalance and selecting a suitable program of art therapy. We also consider the addition of virtual and augmented reality as a further possibility for improving the therapeutic process.
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Battineni, Gopi, Nalini Chintalapudi, and Francesco Amenta. "AI Chatbot Design during an Epidemic like the Novel Coronavirus." Healthcare 8, no. 2 (June 3, 2020): 154. http://dx.doi.org/10.3390/healthcare8020154.
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Since the discovery of the Coronavirus (nCOV-19), it has become a global pandemic. At the same time, it has been a great challenge to hospitals or healthcare staff to manage the flow of the high number of cases. Especially in remote areas, it is becoming more difficult to consult a medical specialist when the immediate hit of the epidemic has occurred. Thus, it becomes obvious that if effectively designed and deployed chatbot can help patients living in remote areas by promoting preventive measures, virus updates, and reducing psychological damage caused by isolation and fear. This study presents the design of a sophisticated artificial intelligence (AI) chatbot for the purpose of diagnostic evaluation and recommending immediate measures when patients are exposed to nCOV-19. In addition, presenting a virtual assistant can also measure the infection severity and connects with registered doctors when symptoms become serious.
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Lamb, H. "News Briefing - Healthcare Doctors slam claims that chatbot is on par with human doctors." Engineering & Technology 13, no. 7 (August 1, 2018): 12. http://dx.doi.org/10.1049/et.2018.0713.
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Judson, Timothy J., Anobel Y. Odisho, Jerry J. Young, Olivia Bigazzi, David Steuer, Ralph Gonzales, and Aaron B. Neinstein. "Implementation of a digital chatbot to screen health system employees during the COVID-19 pandemic." Journal of the American Medical Informatics Association 27, no. 9 (September 1, 2020): 1450–55. http://dx.doi.org/10.1093/jamia/ocaa130.
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Abstract The screening of healthcare workers for COVID-19 (coronavirus disease 2019) symptoms and exposures prior to every clinical shift is important for preventing nosocomial spread of infection but creates a major logistical challenge. To make the screening process simple and efficient, University of California, San Francisco Health designed and implemented a digital chatbot-based workflow. Within 1 week of forming a team, we conducted a product development sprint and deployed the digital screening process. In the first 2 months of use, over 270 000 digital screens have been conducted. This process has reduced wait times for employees entering our hospitals during shift changes, allowed for physical distancing at hospital entrances, prevented higher-risk individuals from coming to work, and provided our healthcare leaders with robust, real-time data for make staffing decisions.
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Sheth, Amit, Hong Yung Yip, Saeedeh Shekarpour, and Amit Sheth. "Extending Patient-Chatbot Experience with Internet-of-Things and Background Knowledge: Case Studies with Healthcare Applications." IEEE Intelligent Systems 34, no. 4 (July 1, 2019): 24–30. http://dx.doi.org/10.1109/mis.2019.2905748.
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Beaudry, Jeremy, Alyssa Consigli, Colleen Clark, and Keith J. Robinson. "Getting Ready for Adult Healthcare: Designing a Chatbot to Coach Adolescents with Special Health Needs Through the Transitions of Care." Journal of Pediatric Nursing 49 (November 2019): 85–91. http://dx.doi.org/10.1016/j.pedn.2019.09.004.
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Castro, Laura, Lucas Baraças, Guilherme Hashioka, and Adriana Carvalho. "376 dCBT-I with Chatbot and Artificial Intelligence: a feasibility study in Brazil." Sleep 44, Supplement_2 (May 1, 2021): A149—A150. http://dx.doi.org/10.1093/sleep/zsab072.375.
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Abstract Introduction Digital cognitive-behavioral therapies for insomnia (dCBT-I) provide low-cost, evidence-based technology, effective in improving mental health and reducing healthcare costs. However, dropout rates still challenge dCBT-I scalability. Moreover, few solutions are available in middle-and-low-income economies where they are most needed. Our goal was to investigate feasibility, describing real-world data and preliminary findings of a novel, fully automated program, developed by Vigilantes do Sono (Sleep Watchers) using Chatbot and Artificial Intelligence (AI). Methods A digital coach interacts with users daily for 5–10 minutes, asking them to complete tailored diaries and delivering CBT-I knowledge pills in ~50 sessions, during ~7 weeks. The Insomnia Severity Index (ISI) is used before and after sleep restriction cycles, weekly revised by an algorithm. Participants (18+ years) were recruited (Jan-Oct/2020) through advertisements on social media, organic search, or were referred by health-care professionals, without face-to-face evaluation. All electronically signed an informed consent. We estimated engagement dividing number of complete diaries by number of days in the program. Generalized Estimating Equations (GEE) evaluated changes in sleep parameters, adjusting for baseline characteristics. Results Of 3,887 individuals who completed initial assessment, 3,139 (81%) had insomnia (ISI ≥11) and 1,489 (42±11 years, 91% women) fulfilled 7+ diaries, commenced sleep restriction, and were included in analysis. Of them, 604 (41%) completed a second ISI and 326 (22%) finished the program. GEE analyzing 42,802 diaries showed sleep duration increased 16.8 (11.9–21.6) minutes from first to second week and 67.3 (52.8–81.8) after week seven; parallel to a relative increase of 34% in sleep efficiency among women and 26% among men. Of 296 participants who reached therapeutic response (ISI reduction ≥8), 66% completed all sessions and 34% crossed half-way. Insomnia remission (ISI≤7) was seen for 55% and 33% of those with subthreshold (n=171) or clinical (n=419) baseline insomnia, respectively. Median (interquartile) engagement was 86% (65–98) and 90% of users recommend the program. Conclusion Chatbot and AI provide a framework to customize dCBT-I and personalize insomnia therapy, potentially favoring engagement and effectiveness. Our findings demonstrate feasibility of the program and support moving forward to continued development and testing the effects in clinical trials. Support (if any):
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Sweeney, Colm, Courtney Potts, Edel Ennis, Raymond Bond, Maurice D. Mulvenna, Siobhan O’neill, Martin Malcolm, et al. "Can Chatbots Help Support a Person’s Mental Health? Perceptions and Views from Mental Healthcare Professionals and Experts." ACM Transactions on Computing for Healthcare 2, no. 3 (July 2021): 1–15. http://dx.doi.org/10.1145/3453175.
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The objective of this study was to understand the attitudes of professionals who work in mental health regarding the use of conversational user interfaces, or chatbots, to support people’s mental health and wellbeing. This study involves an online survey to measure the awareness and attitudes of mental healthcare professionals and experts. The findings from this survey show that more than half of the participants in the survey agreed that there are benefits associated with mental healthcare chatbots (65%, p < 0.01). The perceived importance of chatbots was also relatively high (74%, p < 0.01), with more than three-quarters (79%, p < 0.01) of respondents agreeing that mental healthcare chatbots could help their clients better manage their own health, yet chatbots are overwhelmingly perceived as not adequately understanding or displaying human emotion (86%, p < 0.01). Even though the level of personal experience with chatbots among professionals and experts in mental health has been quite low, this study shows that where they have been used, the experience has been mostly satisfactory. This study has found that as years of experience increased, there was a corresponding increase in the belief that healthcare chatbots could help clients better manage their own mental health.
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Chung, Seung Jin, and Hyunju Lee. "Visual presentation of mental healthcare chatbots for user experience." Journal of the HCI Society of Korea 15, no. 2 (June 30, 2020): 39–45. http://dx.doi.org/10.17210/jhsk.2020.06.15.2.39.
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Siddique, Sarkar, and James C. L. Chow. "Machine Learning in Healthcare Communication." Encyclopedia 1, no. 1 (February 14, 2021): 220–39. http://dx.doi.org/10.3390/encyclopedia1010021.
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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.
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Valtolina, Stefano, Barbara Rita Barricelli, and Serena Di Gaetano. "Communicability of traditional interfaces VS chatbots in healthcare and smart home domains." Behaviour & Information Technology 39, no. 1 (June 30, 2019): 108–32. http://dx.doi.org/10.1080/0144929x.2019.1637025.
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Meadows, Robert, Christine Hine, and Eleanor Suddaby. "Conversational agents and the making of mental health recovery." DIGITAL HEALTH 6 (January 2020): 205520762096617. http://dx.doi.org/10.1177/2055207620966170.
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Background Artificial intelligence (AI) is said to be “transforming mental health”. AI-based technologies and technique are now considered to have uses in almost every domain of mental health care: including decision-making, assessment and healthcare management. What remains underexplored is whether/how mental health recovery is situated within these discussions and practices. Method Taking conversational agents as our point of departure, we explore the ways official online materials explain and make sense of chatbots, their imagined functionality and value for (potential) users. We focus on three chatbots for mental health: Woebot, Wysa and Tess. Findings “Recovery” is largely missing as an overt focus across materials. However, analysis does reveal themes that speak to the struggles over practice, expertise and evidence that the concept of recovery articulates. We discuss these under the headings “troubled clinical responsibility”, “extended virtue of (technological) self-care” and “altered ontologies and psychopathologies of time”. Conclusions Ultimately, we argue that alongside more traditional forms of recovery, chatbots may be shaped by, and shaping, an increasingly individualised form of a “personal recovery imperative”.
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Szmielkin, Polina, Christoph Mingtao Shi, and Andrea Vincenzo Braga. "Chatbots im e-Gesundheitswesen – Ergänzung oder Substitution?" Der Betriebswirt: Volume 59, Issue 1 59, no. 1 (February 28, 2018): 18–24. http://dx.doi.org/10.3790/dbw.59.1.18.
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Abstract Der Wandel der medizinischen Informationsbeschaffung durch Patienten ist für alle Akteure im Gesundheitswesen spürbar. War es in früheren Zeiten gang und gäbe, dass die einzige Informationsmöglichkeit zum Thema Gesundheit der Arzt war, so ist es heute, in Zeiten von sozialen Medien, digitalen Netzwerken und Communities, ein Leichtes, selbst medizinische Informationen aus dem Internet zu erhalten. Neben zahlreichen e-Health- und Telemedizin-Lösungen unterschiedlicher Akteure des Gesundheitswesens, gewinnen nun auch Chatbots zunehmend an Bedeutung. Diese textbasierten, meist auf künstlicher Intelligenz (KI) basierenden Dialogsysteme bergen zahlreiche Chancen, aber auch einige Risiken. Dieser Beitrag stellt mit Hilfe einer SWOT- und PESTLE-Analyse das Potenzial von Chatbots dar und stellt dieses für den deutschen e-Gesundheitsmarkt dar. The change of how patients acquire medical information is noticeable to all actors of the health care system. While in former times it was common practice, that the doctors marked the only source of health information, it has become normal nowadays, in the times of social media, digital networks and communities, to receive medical information from the internet by oneself. Next to numerous e-health and telemedical solutions from various participants in the healthcare system, chatbots increasingly gain relevance. This paper presents the potential of chatbots on the German e-health market with the help of a SWOT and a PESTLE analysis. Keywords: swot analyse, potenzialanalyse, pestle, instant messenger dienste, datenschutz
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Brown, Julia E. H., and Jodi Halpern. "AI chatbots cannot replace human interactions in the pursuit of more inclusive mental healthcare." SSM - Mental Health 1 (December 2021): 100017. http://dx.doi.org/10.1016/j.ssmmh.2021.100017.
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Attrey, Rohin, and Alexander Levit. "The promise of natural language processing in healthcare." University of Western Ontario Medical Journal 87, no. 2 (March 12, 2019): 21–23. http://dx.doi.org/10.5206/uwomj.v87i2.1152.
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The healthcare industry generates data at a rapid rate, with no signs of slowing down. A large portion of this information takes the form of unstructured narrative text, making it difficult for computer systems to analyze the data in a usable format. However, automated analysis of this information could be incredibly useful in daily practice. This could be accomplished with natural language processing, an area of artificial intelligence and computational linguistics that is used to analyze and process large sets of unstructured data, namely spoken or written communication. Natural language processing has already been implemented in many sectors, and the industry is projected to be worth US$16 billion by 2021. Natural language processing could take unstructured patient data and interpret meaning from the text, allowing that information to inform healthcare delivery. Natural language processing can also enable intelligent chatbots, interacting and providing medical support to patients. It has the potential to aid physicians by efficiently summarizing patient charts and predicting patient outcomes. In hospitals, it has the ability to analyze patient satisfaction and facilitate quality improvement. Despite current technical limitations, natural language processing is a rapidly developing technology that promises to improve the quality and efficiency of healthcare delivery.
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Schachner, Theresa, Roman Keller, and Florian v Wangenheim. "Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review." Journal of Medical Internet Research 22, no. 9 (September 14, 2020): e20701. http://dx.doi.org/10.2196/20701.
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Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.
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Guha, Sampurna. "Public perspectives on Healthcare and Artificial Intelligence (AI)." International Journal for Innovation Education and Research 9, no. 7 (July 1, 2021): 1–8. http://dx.doi.org/10.31686/ijier.vol9.iss7.3207.
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Artificial Intelligence is the focus term of the 21st century. It has been widely accepted and adopted in several key areas like education, marketing, retail, and engineering among numerous others however the healthcare sector seems to lag in its welcome. AI has the potential to unlock a new transformation in patient care, diagnosis, and mentoring and support services as seen from a review of relevant literature. It is therefore essential to understand how people view Artificial Intelligence and feel its need or demands in improving their health-related needs. This study was designed, keeping this thought in mind. A survey study was carried out in Delhi-NCR with fifty participants belonging to the age group of 18-50 years, who were health-conscious, and proficient in the use of technology like smartphones and other smart devices. The survey findings reveal that respondents desired the integration of Artificial Intelligence-based solutions to medical services ranging from appointment booking through intelligent chatbots to insightful diagnosis using risk profiles, intricate surgeries guided by intelligent robots to mentoring services which described health goals and discussed sustainable solutions towards achieving desired goals through lifestyle changes. Secondary data showed the application of such technologies in hospitals in form of unique personalized programs aimed at improving patient care and promote good health. The study further recommends the use of Artificial Intelligence in healthcare services in both rural and urban areas to reduce the burden on medical professionals, increase personalized and efficient healthcare service provision especially in times of global pandemic and increase people’s consciousness towards the need for good health through the adoption of positive lifestyle changes.
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Jeyarani Periyasamy, Muqaddas Rahim, and Kalaimagal Ramakrishnan. "Diabetes Prediction System using Classification Techniques & Healthcare Consultation using Artificial Intelligence." Asia Proceedings of Social Sciences 7, no. 2 (March 28, 2021): 194–98. http://dx.doi.org/10.31580/apss.v7i2.1883.
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Diabetes is a global diseases that has affected over 388 million people and cause many deaths and serious condition. This is due to the late detection and diagnosis of the disease as it causes a delay in treatment and becomes harder to prevent it from worsening. It is important to detect the disease at an early stage and start early treatment to prevent it from becoming life-threatening. The aim of this project is to produce a system that can accurately predict the disease in real-time for the user and provide online consultation by doctors and chatbots which will help prevent major illnesses in future. The project targets anyone who may want to check whether they have the disease or not. It also serves as a platform for doctors to provide online consultation to their clients. The project will follow the Knowledge Discovery in Database approach. Implementing the system will reduce time consumption, produce real-time results cost-freely & early detection of diabetes. The project is expected to produce a functional system which accurately predicts diabetes based on the data entered in real-time to minimize visits to clinics and cut the cost of the test while providing online health consultation.
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Shinde, Amol, Dilip Pawar, and Kunal Sonawane. "Automation in pharmaceutical sector by implementation of artificial intelligence platform: a way forward." International Journal of Basic & Clinical Pharmacology 10, no. 7 (June 22, 2021): 863. http://dx.doi.org/10.18203/2319-2003.ijbcp20212387.
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Worldwide, there are technological advances that swift automation in several aspects of the pharmaceutical industry such as pharmacovigilance, clinical research, medical affairs, and marketing. Innovative technology like artificial intelligence (AI) emphasizes the massive use of the internet for drug development, drug safety, data analytics, communication marketing, and customer engagement to achieve the goal of pharmaceuticals and patient-centric healthcare. Presently, escalating the number of individual case safety reports (ICSRs) necessitate the support of AI in the transformation of drug safety professional. AI can be transformed and evolve the clinical trial process from the conventional method alongside benefited the cutting cost, enhancing the trial quality, and alleviate trial time by almost half. Today, AI may be efficiently implemented to lower the cost of medical information requests, besides the online chatbots to communicate with health care professionals (HCPs) and consumers. There are numerous forthcoming uses of AI which need to be executed for renovation in the field of pharmaceuticals.
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Cousino, Melissa K., Kelly E. Rea, and Lauren M. Mednick. "Understanding the healthcare communication needs of pediatric patients through the My CHATT tool: A pilot study." Journal of Communication in Healthcare 10, no. 1 (January 2, 2017): 16–21. http://dx.doi.org/10.1080/17538068.2017.1278637.
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Hari, Daniel, Valentino Šafran, Umut Arioz, Izidor Mlakar, Matej Rojc, Gazihan Alankus, and Rafael Perez Luna. "Multilingual Conversational Systems to Drive the Collection of PROs and Integration into Clinical Workflow." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (August 5, 2021): 113–18. http://dx.doi.org/10.37394/23208.2021.18.13.
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Collection of patient-reported outcomes (PROs) remotely and their usage in the clinical workflow provide an improvement on both patient’s quality of life and cancer care. However, adoption of collecting PROs into the clinical workflow is rare, and existing works still have a lot of issues providing a holistic approach. This paper offers enhancements in the process of collecting PROs by utilization of conversational systems that still provide quite a new but promising way to collect PROs remotely with spoken interaction. Our proposed system provides an interoperability with Fast Healthcare Interoperability Resources (FHIR) server by using a multimodal sensing network (MSN) prepared for the project PERSIST. We introduce components of multimodality while collecting PROs with the help of the mHealth App and Open Health Connect (OHC) platform. As a result, chatbots and 3D embodied conversational agents (ECA) were prepared to interact with the cancer patients in 5 different languages. The intercommunication was provided by MSN and the integration of cancer patients’ PROs into clinical workflow was satisfied. This study was part of a Horizon 2020 project and a preparation phase for clinical trials with cancer patients and clinicians
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Aligato, Mila F., Vivienne Endoma, Jonas Wachinger, Jhoys Landicho-Guevarra, Thea Andrea Bravo, Jerric Rhazel Guevarra, Jeniffer Landicho, Shannon A. McMahon, and Mark Donald C. Reñosa. "‘Unfocused groups’: lessons learnt amid remote focus groups in the Philippines." Family Medicine and Community Health 9, Suppl 1 (August 2021): e001098. http://dx.doi.org/10.1136/fmch-2021-001098.
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The ongoing COVID-19 pandemic has required tremendous shifts in data collection techniques. While an emerging body of research has described experiences conducting remote interviews, less attention has been paid to focus group discussions (FGDs). Herein, we present experiences conducting remote FGDs (n=9) with healthcare workers and caretakers of small children in the Philippines. We used ‘Facebook Messenger Room’ (FBMR), the preferred platform of participants. Despite some success, we generally encountered considerable challenges in terms of recruiting, retaining and moderating remote FGDs, particularly among caretakers of small children. Finding a quiet, private place proved unfeasible for many participants, who were juggling family demands in tight, locked down quarters. Connectivity issues and technological missteps compromised the flow of FGDs and minimised the ability to share and compare opinions. For the research team, remote FGDs resulted in a dramatic role shift for notetakers—from being passive observers to active tech supporters, chatbox referees and co-moderators (when audio disruptions occurred). Finally, we note that remote FGDs via FBMR are associated with ethical complexities, particularly as participants often chose to use their personal Facebook accounts, which can compromise anonymity. We developed and continuously refined strategies to mitigate challenges, but ultimately decided to forgo FGDs. We urge fellow researchers with more successful experiences to guide the field in terms of capturing high-quality data that respond to research questions, while also contending with privacy concerns, both in online spaces, as well as physical privacy despite lockdowns in tight quarters.
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Yigitcanlar, Tan, Nayomi Kankanamge, Massimo Regona, Andres Ruiz Maldonado, Bridget Rowan, Alex Ryu, Kevin C. Desouza, Juan M. Corchado, Rashid Mehmood, and Rita Yi Man Li. "Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?" Journal of Open Innovation: Technology, Market, and Complexity 6, no. 4 (December 11, 2020): 187. http://dx.doi.org/10.3390/joitmc6040187.
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Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.
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Yigitcanlar, Tan, and Federico Cugurullo. "The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities." Sustainability 12, no. 20 (October 15, 2020): 8548. http://dx.doi.org/10.3390/su12208548.
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The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics the behaviors commonly associated with human intelligence. Today, various AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. More recently, AI applications have also started to become an integral part of many urban services. Urban artificial intelligences manage the transport systems of cities, run restaurants and shops where every day urbanity is expressed, repair urban infrastructure, and govern multiple urban domains such as traffic, air quality monitoring, garbage collection, and energy. In the age of uncertainty and complexity that is upon us, the increasing adoption of AI is expected to continue, and so its impact on the sustainability of our cities. This viewpoint explores and questions the sustainability of AI from the lens of smart and sustainable cities, and generates insights into emerging urban artificial intelligences and the potential symbiosis between AI and a smart and sustainable urbanism. In terms of methodology, this viewpoint deploys a thorough review of the current status of AI and smart and sustainable cities literature, research, developments, trends, and applications. In so doing, it contributes to existing academic debates in the fields of smart and sustainable cities and AI. In addition, by shedding light on the uptake of AI in cities, the viewpoint seeks to help urban policymakers, planners, and citizens make informed decisions about a sustainable adoption of AI.
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Akter, Raushan, M. Jalal Uddin, and Rajat Sankar Roy Biswas. "Disease Pattern at Medicine Outpatient Department of A Tertiary Care Hospital." Chattagram Maa-O-Shishu Hospital Medical College Journal 18, no. 1 (July 10, 2019): 27–30. http://dx.doi.org/10.3329/cmoshmcj.v18i1.42129.
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Background : Bangladesh, like many transitional nations, is straddling with the demographic and epidemiological transition. There is a critical need to improve public health in this region. But number of studies & representative data on the prevalence of diseases is inadequate. The objective of this study is to detect type & frequency of diseases among patients attending in medicine outpatient department (OPD) to improve the quality of healthcare.
Materials and methods: This observational study was conducted at the outpatient department (OPD) of Chattogram Maa-O-Shishu Hospital Medical College from February to April 2018. Purposive sampling was used. Total 500 patients were included. Details were recorded in a data form and diagnosis was made on the basis of history, physical examination and necessary laboratory investigations. Data were collected and analyzed using the SPSS Version 20.
Results : Total 500 patients were evaluated. Majority were female (61.2%). Highest number of patients i.e. 299 (59.4%) belonged to the age group of 16–35 years. Majority 405 (81%) of the patients in our study were from surrounding locality (Urban). The most common diseases was DM affecting 55(11%). HTN was 2nd common disease 51(10.2%). During this study we found gastrointestinal system was the most common affected organ system.
Conclusion: Disease pattern study is very important to focus top problems, so that we can prepare ourselves to fight against them.
Chatt Maa Shi Hosp Med Coll J; Vol.18 (1); Jan 2019; Page 27-30
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"The Smart Health Care Prediction using Chatbot." Regular 9, no. 2 (July 30, 2020): 75–78. http://dx.doi.org/10.35940/ijrte.a3007.079220.
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Our healthcare is very much important to lead a peaceful and honest life. If any health issue occurs, we need to go to the hospital and consult the doctor for the very minor problems. our healthcare chatbot is developed to help the people to predict their health issue early at home before they visit the doctor or hospital for the mi nor problems. For the minor issues we are spending lots of costs. The healthcare chatbot is design to reduce such costs and also its improves the efficiency of the medical healthcare. There were many chatbots available they act as a reference for the patient to know more about their health issue. The healthcare chatbot is something different from the other chatbots which predicts the diseases by using symptoms and gives the doctor details to consult the doctor. The healthcare chatbot is developed by using AI in the text to text conversation mode. The user who knows only to write and read can use this chatbot for their minor issue. In this healthcare chatbot, the system predicts the diseases based on the symptom given by the user using the pattern concept in AIML algorithm. The system also predicts the prescription and also give the doctor details to the user based on the diseases predicted for their symptom given. By using this healthcare chat bot people will know the minor diseases at early stage with no costs. Whenever the patient or user gets the time they will consult doctor for their health issue. This will make people to know more about their health issue anywhere at any time.
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Potts, C., E. Ennis, R. B. Bond, M. D. Maurice, M. F. McTear, K. Boyd, T. Broderick, et al. "Chatbots to Support Mental Wellbeing of People Living in Rural Areas: Can User Groups Contribute to Co-design?" Journal of Technology in Behavioral Science, September 20, 2021. http://dx.doi.org/10.1007/s41347-021-00222-6.
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AbstractDigital technologies such as chatbots can be used in the field of mental health. In particular, chatbots can be used to support citizens living in sparsely populated areas who face problems such as poor access to mental health services, lack of 24/7 support, barriers to engagement, lack of age appropriate support and reductions in health budgets. The aim of this study was to establish if user groups can design content for a chatbot to support the mental wellbeing of individuals in rural areas. University students and staff, mental health professionals and mental health service users (N = 78 total) were recruited to workshops across Northern Ireland, Ireland, Scotland, Finland and Sweden. The findings revealed that participants wanted a positive chatbot that was able to listen, support, inform and build a rapport with users. Gamification could be used within the chatbot to increase user engagement and retention. Content within the chatbot could include validated mental health scales and appropriate response triggers, such as signposting to external resources should the user disclose potentially harmful information or suicidal intent. Overall, the workshop participants identified user needs which can be transformed into chatbot requirements. Responsible design of mental healthcare chatbots should consider what users want or need, but also what chatbot features artificial intelligence can competently facilitate and which features mental health professionals would endorse.
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"Adaptive Machine Learning Chatbot for Code-Mix Language (English and Hindi)." International Journal of Recent Technology and Engineering 8, no. 5 (January 30, 2020): 3566–72. http://dx.doi.org/10.35940/ijrte.e6489.018520.
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The humanoid assistant system can be described as the system resembling or imitating the human behaviour. These systems can be called as chatbots. There are a large number of conventional scripted types of chatbots. The problem with these chatbots is that they provide a monotonous type of communication i.e. they provide the user with a predefined set of options for any of its query. This scripted nature limits the scope of the chatbot systems, to provide smart and effective services to the users. This problem restricts the system efficiency. Efforts are being made to improve the scripted nature of chatbots and enable them to converse in a manner similar to the conversation between two humans. This makes the system more user-friendly, and provides better solutions to them. Chatbots providing health care services imitate the conversation between the doctor and the patients to give them general information about diseases, remedies, precautions, etc. and also provides a prediction of the diseases depending upon the symptoms provided by the user. Here, the chatbot behaves as a virtual doctor. This can be achieved by incorporating NLU, ML and NLG techniques in the system. Here, in this paper, we have briefed about the chatbot system architecture and adaptive self-learning algorithm for providing services in healthcare domain
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"Ai Healthcare Interactive Talking Agent using Nlp." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (November 10, 2019): 3470–73. http://dx.doi.org/10.35940/ijitee.a4915.119119.
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Good nutrition plays an important part in leading an active lifestyle. Combined with physical exercises, the diet can benefit people to maintain their weight, reduce the possibility of diseases and improve overall health. A self-help motivational tool for weight maintenance is a good option. This paper presents an interactive talking agent that is a chatbot. A chatbot is a piece of software that operates a conversation using textual methods. Chatbot will start communication with the user and help to solve the concern by initiating a human way conversation using Language Understanding Intelligence Service (LUIS) concept. Natural language processing (NLP) is the capability of a computer application for understanding human dialect. It is one of the part of Artificial Intelligence (AI). Each language has a different morphology the chatbot has to be able to separate words into individual morphemes. Morphology is one of the tasks that NLP should be able to handle.
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S, Shraddha A., Shreepada Bhat, Shubhashri V. K, Sinchana Karnik, and Narender M. "Disease Prediction Chatbot." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, June 20, 2021, 632–36. http://dx.doi.org/10.32628/cseit2173172.
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Applications in the field of machine learning and artificial intelligence have been in great demand over the recent decade. Now it has various applications in the field of health industry. With the help of machine learning algorithm prediction of diseases has been made easier. Now doctors can concentrate only on treatment with the help of technology. Technology is accelerating innovations in the healthcare domain which has increased people’s standard of living over the years. Here in our project we are making a healthcare chatbot with help of Natural language processing and machine learning algorithm to predict disease. User interacts with the chatbot just like one interacts with his doctor and based on the symptoms provided by users and the chatbot will identify the symptom and predict the disease.
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"MedBot: Conversational Artificial Intelligence Powered ChatBot for Health Care." International Journal of Advanced Trends in Computer Science and Engineering 10, no. 2 (April 5, 2021): 1150–52. http://dx.doi.org/10.30534/ijatcse/2021/941022021.
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Healthcare is essential for leading a happy life. However, It is exceedingly difficult to seek a doctor's appointment in the event of a health condition. The suggested solution is to create a medical chatbot that can diagnose the disease and provide basic information about it before consulting a doctor. The medical chatbot was created with the aim of lowering healthcare costs and increasing access to medical information. The medical chatbot was developed with the aim of lowering healthcare costs. This system will improve processes services and outcomes and reduce cash. In a self-service mode, users communicate with the ChatBot through text. Healthcare ChatBot was developed with the aim of maintaining internal documents. It operates as a medical reference guide, assisting patients in learning about their condition and determining the disease's status based on symptoms. The main aspect of the Healthcare ChatBot is that the user can connect with the Bot directly without any restrictions, and that the user can use the app at any time without hesitation and without having to pay any money to learn about their condition. We will add more modules in the future, such as doctors and local hospitals, so that users have a better of what they want to do next. Patients can talk to a text-to-text diagnosis bot about their health conditions, and the bot will give them a customized diagnosis based on their understanding symptoms. As a result, people will be more aware of their health, and they will be better protected.
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Tiwari, Shivanand. "Acute Diseases Prognosis using Chatbot." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021). http://dx.doi.org/10.22214/ijraset.2021.36422.
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The role of chatbots in healthcare is to help free-up valuable physician-time by reducing or eliminating unnecessary doctor’s appointments. As the increase in cost, various healthcare organizations are looking for different ways to manage cost while improving the user’s experience. As we know there is shortage of healthcare professionals that makes it increasingly necessary for us to augment technology with health facilities in order to allow doctors to focus on more critical patient needs. Keeping this in Mind we are aiming to develop a Project that will basically ask for Symptoms from the Patient and perform the Prognosis on the basis of already developed dataset. The Machine Learning Algorithm will work on that dataset of symptoms and their prognosis to tell exactly what has happened to the Patient and will help to Reach the Desired Consultant/Doctor with respect to the Prognosis. It will also help the Patients to get Useful Information regarding different diseases that may help to deal with some Chronic Diseases at an early Stage!’
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Nivedhitha, G., E. Punarselvam, K. R. Aaghash, M. Elayabarathi, K. Rahul, and R. Santhosh. "Ai Consulting Healthcare Chatbot System Using Pattern Matching." International Journal of Scientific Research in Science and Technology, January 5, 2021, 18–22. http://dx.doi.org/10.32628/ijsrst2182112.
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In today's world there are millions of diseases with various symptoms foreach, no human can possibly know about all of these diseases and the treatmentsassociated with them. So, the problem is that there isn’t any place where anyone can have the details of the diseases or the medicines/treatments. What if there is a placewhere you can find your health problem just by entering symptoms or the currentcondition of the person. It will help us to deduce the problem and to verify thesolution. The proposed idea is to create a system with artificial intelligence that canmeet these requirements. The AI can classify the diseases based on the symptomsand give the list of available treatments. The System is a text-to-text diagnosis chatbot that will engage patients in conversation with their medical issues and provides apersonalized diagnosis based on their symptoms and profile. Hence the people canhave an idea about their health and can take the right action.
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Geoghegan, L., A. Scarborough, J. C. R. Wormald, C. J. Harrison, D. Collins, M. Gardiner, J. Bruce, and J. N. Rodrigues. "Automated conversational agents for post-intervention follow-up: a systematic review." BJS Open 5, no. 4 (July 1, 2021). http://dx.doi.org/10.1093/bjsopen/zrab070.
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Abstract Background Advances in natural language processing and other machine learning techniques have led to the development of automated agents (chatbots) that mimic human conversation. These systems have mainly been used in commercial settings, and within medicine, for symptom checking and psychotherapy. The aim of this systematic review was to determine the acceptability and implementation success of chatbots in the follow-up of patients who have undergone a physical healthcare intervention. Methods A systematic review of MEDLINE, MEDLINE In-process, EMBASE, PsychINFO, CINAHL, CENTRAL and the grey literature using a PRISMA-compliant methodology up to September 2020 was conducted. Abstract screening and data extraction were performed in duplicate. Risk of bias and quality assessments were performed for each study. Results The search identified 904 studies of which 10 met full inclusion criteria: three randomised control trials, one non-randomised clinical trial and six cohort studies. Chatbots were used for monitoring after the management of cancer, hypertension and asthma, orthopaedic intervention, ureteroscopy and intervention for varicose veins. All chatbots were deployed on mobile devices. A number of metrics were identified and ranged from a 31 per cent chatbot engagement rate to a 97 per cent response rate for system-generated questions. No study examined patient safety. Conclusion A range of chatbot builds and uses was identified. Further investigation of acceptability, efficacy and mechanistic evaluation in outpatient care pathways may lend support to implementation in routine clinical care.
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"Diabot: A Predictive Medical Chatbot using Ensemble Learning." International Journal of Recent Technology and Engineering 8, no. 2 (July 30, 2019): 6334–40. http://dx.doi.org/10.35940/ijrte.b2196.078219.
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Accessibility to medical knowledge and healthcare costs are the two major impediments for common man. Conversational agents like Medical chatbots, which are designed keeping in view medical applications can potentially address these issues. Chatbots can either be generic or disease-specific in nature. Diabetes is a non-communicable disease and early detection of the same can let people know about the serious consequences of this disorder and help save human lives. In this paper, we have developed a generic text-to-text ‘Diabot’ – a DIAgnostic chatBOT which engages patients in conversation using advanced Natural Language Understanding (NLU) techniques to provide personalized prediction using the general health dataset and based on the various symptoms sought from the patient. The design is further extended as a DIAbetes chatBOT for specialized Diabetes prediction using the Pima Indian diabetes dataset for suggesting proactive preventive measures to be taken. For prediction, there exists multiple classification algorithms in Machine Learning which can be used based on their accuracy. However, rather than considering only one model and hoping this model is the best or most accurate predictor we can make, the novelty in this paper lies in Ensemble learning, which is a meta-algorithm that combines a myriad of weaker models and averages them to produce one final balanced and accurate model. From literature reviews, it is observed that very little research has happened in ensemble methods to increase prediction accuracy. The paper presents a state-of-the art Diabot design with an undemanding front-end interface for common man using React UI, RASA NLU based text pre-processing, quantitative performance comparison of various machine learning algorithms as standalone classifiers and combining them all in a majority voting ensemble. It is observed that the chatbot is able to interact seamlessly with all patients based on the symptoms sought. The accuracy of Ensemble model is balanced for general health prediction and highest for diabetes prediction among all weak learners considered which provides motivation for further exploring ensemble techniques in this domain.
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Choi, Yunjung, and Hyun-Sook Kim. "Feasibility and Acceptability of ICT (Information and Communications Technologies) Based Mobile Chatbot Technology to Reduce Dietary Sugar Intake (P04-004-19)." Current Developments in Nutrition 3, Supplement_1 (June 1, 2019). http://dx.doi.org/10.1093/cdn/nzz051.p04-004-19.
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Abstract Objectives A high level of dietary sugars intake is of concern because of its association with poor dietary quality, obesity, and risk of Noncommunicable diseases (NCDs). The ICT-based healthcare has been increasingly perceived to be an effective way to dietary assessment and monitoring. We engaged a mobile chat robot which can monitor dietary sugar intake after recording dietary intake and provide education message on the reduction of dietary sugar intake. Here, we examined the feasibility and acceptability of ICT based mobile chatbot to reduce dietary sugar intake. Methods In a two-month pre–post intervention study, 68 female college students aged 18–25 years consented and participated in this study. Participants were instructed to record all foods and beverages consumed using text mode input daily and received education message on the reduction of sugar intake once a week. At post intervention, participants were asked to answer the questionnaire about feasibility and acceptability test of chatbot by using Likert 5-point scale. Results The 91.2% of subjects reported chatbot was easy to use, and the 72% of participants answered the chatbot was easy to monitor their sugar intake. Among subjects, the 88.2% reported that they realized about their own dietary sugar intake, and the 92.6% answered the education message was very helpful to reduce dietary sugar intake. Meanwhile, 64.7% reported the burdensome to use the chat robot, and the 69.1% had trouble remembering to record their food intake. Conclusions ICT based mobile chatbot technology is represented to be feasible and acceptable in this study. Almost all participants reported that it is easy to use and monitor their sugar intake, and they were able to learn about nutrition education including monitoring sugar intake. The use of chatbot, however, can sometimes be a burden. Additional research is needed to ease the burden of participants to draw better and more accurate data. Funding Sources Department of Food and Nutrition, Sookmyung Women's University, Republic of Korea.
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Parviainen, Jaana, and Juho Rantala. "Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care." Medicine, Health Care and Philosophy, September 4, 2021. http://dx.doi.org/10.1007/s11019-021-10049-w.
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AbstractMany experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. The COVID-19 pandemic, however, has significantly increased the utilisation of health-oriented chatbots, for instance, as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence clinical practices and expert–client relationships. We suggest the need for new approaches in professional ethics as the large-scale deployment of artificial intelligence may revolutionise professional decision-making and client–expert interaction in healthcare organisations. We argue that the implementation of chatbots amplifies the project of rationality and automation in clinical practice and alters traditional decision-making practices based on epistemic probability and prudence. This article contributes to the discussion on the ethical challenges posed by chatbots from the perspective of healthcare professional ethics.
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Lin, Julia, Todd Joseph, Joanna Jean Parga-Belinkie, Abigail Mandel, Ryan Schumacher, Karl Neumann, Laura Scalise, et al. "Development of a practical training method for a healthcare artificial intelligence (AI) chatbot." BMJ Innovations, December 15, 2020, bmjinnov—2020–000530. http://dx.doi.org/10.1136/bmjinnov-2020-000530.
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Chow, James, and Lu Xu. "Chatbot for Healthcare and Oncology Applications using Artificial Intelligence and Machine Learning (Preprint)." JMIR Cancer, February 9, 2021. http://dx.doi.org/10.2196/27850.
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Turk, Philip J., Thao P. Tran, Geoffrey A. Rose, and Andrew McWilliams. "A predictive internet-based model for COVID-19 hospitalization census." Scientific Reports 11, no. 1 (March 3, 2021). http://dx.doi.org/10.1038/s41598-021-84091-2.
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AbstractThe COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.
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Hautz, Wolf E., Aristomenis Exadaktylos, and Thomas C. Sauter. "Online forward triage during the COVID-19 outbreak." Emergency Medicine Journal, December 11, 2020, emermed-2020-209792. http://dx.doi.org/10.1136/emermed-2020-209792.
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Health systems face major challenges during the COVID-19 pandemic with new information and challenges emerging daily and frequently changing guidelines. Online forward triage tools (OFTTs) provide useful information, direct patients and free physician resources.We implemented an OFTT targeted at the current pandemic, adapted the content and goals and assessed its effects. The OFTT was implemented on 2 March 2020 and modified regularly based on the revised testing criteria issued by the Swiss Federal Office of Public Health. After testing criteria liberalised, a chatbot tool was set up on 9 April 2020 to assess urgency of testing, referral to available testing sites and need for emergency care.In the first 40 days of the OFTT, there were more than 17 300 visitors and 69.8% indicated they would have contacted the healthcare system if the online test had not been available. During the initial week of operation, using the conservative testing strategy, 9.1% of visitors received recommendations to be tested, which increased to 36.0% of visitors after a change in testing criteria on 9 March 2020. Overall, since the implementation of the tool, 26.27% of all users of the site have been directed to obtain testing. The Chatbot tool has had approximately 50 consults/day.Setting up an OFTT should be considered as part of local strategies to cope with the COVID-19 pandemic. It may ease the burden on the healthcare system, reassure patients and inform authorities. To account for the dynamic development of the pandemic, frequent adaptation of the tool is of great importance. Further research on clinical outcomes of OFTT is urgently needed.