Institute of Learning, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health, Dubai, UAE.
Stud Health Technol Inform. 2024 Aug 22;316:1524-1528. doi: 10.3233/SHTI240705.
In medical education, case-based learning (CBL) is a fundamental method for training healthcare professionals across different levels of expertise. This approach hinges on using authentic or fabricated clinical cases to bridge the gap between theoretical knowledge and its practical application. It fosters active engagement and knowledge application among learners in healthcare domains. While creating effective cases demands substantial clinical understanding and time investment, the integration of Generative Artificial Intelligence (AI) presents a promising solution to this challenge. AI can efficiently analyze extensive medical data to generate diverse and realistic clinical scenarios, continuously updating case content based on emerging medical literature and guidelines. This study explores AI-generated cases' feasibility and educational value in continuing medical education, focusing on COVID-19 scenarios tailored for the MENA region. Results indicate the potential of AI-generated cases to foster engagement and critical thinking among learners, suggesting their suitability for different levels of education. This study highlights the advantages of integrating AI into CBL and emphasizes the need for future efforts to tackle obstacles and facilitate its successful adoption.
在医学教育中，基于案例的学习（CBL）是培训不同专业水平医疗保健专业人员的基本方法。这种方法依赖于使用真实或虚构的临床案例来弥合理论知识与其实际应用之间的差距。它促进了医疗领域学习者的积极参与和知识应用。虽然创建有效的案例需要大量的临床理解和时间投入，但生成式人工智能（AI）的整合为这一挑战提供了一个有前途的解决方案。AI 可以有效地分析大量的医疗数据，生成多样化和现实的临床场景，并根据新兴的医学文献和指南不断更新案例内容。本研究探讨了 AI 生成案例在继续医学教育中的可行性和教育价值，重点是为 MENA 地区量身定制的 COVID-19 场景。结果表明，AI 生成案例有可能促进学习者的参与和批判性思维，表明它们适合不同层次的教育。本研究强调了将 AI 整合到 CBL 中的优势，并强调需要未来努力解决障碍，促进其成功采用。
