Machine learning's impact on medical education and research: beneficial or detrimental?

Suresh Kanna Subramaniam, Pushpalatha Kunjappa Gounder, Ammar Abdul Razzak Mahmood, Harishchander Anandaram, Georgiy Georgievich Polevoy, Muthu Prasanna Pichandy


Machine learning (ML), an AI chatbot developed by OpenAI, has the potential to revolutionize medical education by aiding in locating scholarly publications, condensing them, producing automatic drafts, summarising articles, and translating information from various languages. Still ethical concerns need to be governed and closely supervised in scientific literature. ML has become a valuable tool for medical research and teaching due to its ability to generate responses that closely resemble human responses when faced with difficult medical questions. It has disadvantages such as the potential dissemination of inaccurate or prejudiced data and excessive dependence on technology in medical instruction, deteriorating analytical reasoning and clinical judgment abilities. ML can aid in various aspects of medical education, including curriculum building, tutoring, test preparation, medical research, simulation, and continuing medical education. This article explores the transformative impact of ML in the medical field, focusing on medical data analysis, rewards in medical education, enhanced diagnosis, and creative content generation. It delves into ML applications for medical learners and educators, including interactive simulations, cooperation enhancement, and clinical vignettes. The article also addresses ML's role in patient care, along with strategies, challenges, and limitations in its implementation.

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International Journal of Public Health Science (IJPHS)
p-ISSN: 2252-8806, e-ISSN: 2620-4126

This journal is published by the Intelektual Pustaka Media Utama (IPMU) in collaboration with Institute of Advanced Engineering and Science (IAES).

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