Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Exploring the Impact of Chat GPT on Medical Education and Research: A Comprehensive Review (Preprint)
0
Zitationen
4
Autoren
2024
Jahr
Abstract
<sec> <title>BACKGROUND</title> AI has significantly impacted medicine, medical education, and research. Chat GPT, an AI-based application, was introduced in 2018 and has revolutionized medical education and research by enhancing learning, engaging students, and aiding critical thinking. It also aids in patient management and research by retrieving data quickly. However, it entails challenges like ethical concerns, responsibility, plagiarism, and data authenticity. </sec> <sec> <title>OBJECTIVE</title> This systematic review provides insights into the benefits, challenges, and directions of Chat GPT in medical education and research. </sec> <sec> <title>METHODS</title> This systematic review reviewed the use of Chat GPT in medical education and research, focusing on English language studies from January 2023 to December 2023. The review includes studies published from November 2022 onwards. This includes journal articles, editorials, case reports, letters to editors, conference papers, meeting papers, and dissertations. The study excluded studies using Chat GPT in medical research and education-related languages other than English, studies of less than two pages, 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, abstract, and full text. The review adhered to the PRISMA guidelines for systematic reviews and meta-analyses. </sec> <sec> <title>RESULTS</title> Chat GPT, a chatbot used in medical education and research, offers information on qualifications, responsibilities, training, and community health outcomes regarding medical education& medical research. It facilitates asynchronous communication, timely feedback, and personalized learning experiences. Chat GPT can improve patient outcomes, enhance in-person office operations, and improve patient monitoring. It can answer medical questions with 80% accuracy, but risks like inaccurate information dissemination and ethical concerns must be considered. It enhances the critical skills of medical professionals, enhancing their knowledge and confidence in making effective clinical decisions. However, ethical concerns such as patient privacy and data bias, must be considered. Chat GPT has proven to be effective in passing the USMLE exam, but invokes concerns about academic integrity. More research is needed to fully explore its potential in medical education & research. Medical professionals in developing countries lack knowledge about AI tools and show ethical concerns regarding patient identity protection, data bias elimination, accuracy, and transparency. The ChatGPT-4 tool raises ethical concerns such as potential bias and has lower accuracy scores for complex medical questions. Healthcare professionals misuse chat GPT and AI tools in medical writing and research, posing ethical and copyright issues. They also face challenges in critical thinking, information accuracy, language barriers, and ethical considerations. AI models are still in their early stages, but they can offer practical solutions. Technical 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. </sec> <sec> <title>CONCLUSIONS</title> Chat GPT 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, responsibility, and accountability in patient management as well as in research projects and draft writing. </sec>
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.391 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.257 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.685 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.501 Zit.