چالش های استفاده از هوش مصنوعی در حوزه آموزش پزشکی
کد: G-1351
نویسندگان: Neda Jalili * ℗, Milad Ahmadi Marzaleh
زمان بندی: زمان بندی نشده!
برچسب: دستیار مجازی هوشمند
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background: Artificial Intelligence is the biggest game-changer for medical education. AI-generated visual art, cutting-edge adaptive learning systems, and large language models - like ChatGPT- will equip students with visualization, personalized learning, and enhanced clinical decision-making skills. However, the challenges of incorporating such technologies into medical education include accuracy, ethics, and resource demands in education systems as diverse as China's secondary vocational system and the specialism of radiation oncology. Methods: These authors have reviewed the narrative literature and synthesized data/findings from five articles on the use of artificial intelligence in medical education: two sets of qualitative and quantitative studies encompassing one assessing benefits of AI-generated art and challenges associated with it, another assessing ChatGPT performance in radiation oncology exams and cases, a third mirroring this assessment, a fourth describing reforms to vocational medical education in China, and the fifth looks at mHealth preferences among low-income pregnant women (used here for broader AI comparisons). The researchers systematically reviewed PubMed, Web of Science, and Scopus databases for studies published since December 2015. Data were narratively synthesized to identify common barriers across contexts. Results: AI in medical education visualizes complex concepts such as anatomy, personalizes educational content, and simulates clinical scenarios. For example, ChatGPT proved 74.57% accurate on the ACR TXIT exam and suggested several new cases for the Gray Zone. High investment and training of educators will be needed for implementing AI, with some resistance to changing traditional systems. Furthermore, because ChatGPT cannot analyze medical images, its reliability is reduced by its surface understanding of several domains; hence, it must be duly verified and fine-tuned for specific domains. Conclusion: The barriers include accuracy, ethical issues, and the question of equal resources that deter mass application. To move on, one has to work on standardized assessments, robust ethical frameworks, and the provision of infrastructure and training. The following steps in this direction must include developing AI tools for specific medical areas and disseminating them among different educational platforms, thereby enhancing the advantages of AI in education.
کلمات کلیدی
Artificial Intelligence, Medical Education, Ethics
