Exploring the Impact of ChatGPT on Medical Education and Research: A Systematic Review (82812)

Session Information: AI & Education
Session Chair: Sinokubekezela Princess Dube

Saturday, 13 July 2024 17:35
Session: Session 5
Room: G09 (Ground)
Presentation Type:Oral Presentation

All presentation times are UTC0 (Europe/London)

This systematic review provides insights into the benefits, challenges, and directions of ChatGPT in medical education and research. This systematic review reviewed the use of ChatGPT in medical education and research, focusing on English language studies from January to December 2023. This includes journal articles, editorials, case reports, letters to editors, conference papers, and meeting papers. The study excluded studies in languages other than English, book chapters, and studies on management sciences, engineering, social sciences, media and IT. The PRISMA diagram outlines the process of selecting 50 studies qualifying for inclusion. These were analyzed using a material extraction structure. The studies included after evaluating titles, abstracts and full text. Methods: ChatGPT, a chatbot used in medical education and research, training, and community health outcomes regarding medical education and medical research. It facilitates asynchronous communication, timely feedback, and personalised learning experiences. ChatGPT can improve patient outcomes, enhance in-person office operations, and improve patient monitoringTechnical barriers, such as natural language processing, may lead to misunderstandings. AI tools pose academic integrity concerns in medical education and research, and medical educators must adapt to technology changes. ChatGPT provides learning opportunities in the form of self-directed learning and helps in passing exams. It provides an innovative methodology for establishing clinical diagnosis and decision making as well as management plans for patients. It helps in patient education as well as medical research. It is, however, associated with certain challenges like limitation of data, biased data, inaccuracy of data, plagiarism, data privacy, patients’ confidentiality, and responsibility.

Authors:
Shazia Rasul, Shalamar Medical and Dental College, Pakistan
Ghulam Farid, Shalamar Medical and Dental College, Pakistan
Anila Jalil, Shalamar Medical and Dental College, Pakistan
Zahid Bashir, Shalamar Medical and Dental College, Pakistan


About the Presenter(s)
Dr Shazia Rasul, currently working as Associates Professor OBGYN / Adjunct Faculty Medical Education in Shalamar Medical &Dental College, Lahore, Pakistan

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00