Acad Med. 2024 Apr 1;99(4S Suppl 1):S77-S83. doi: 10.1097/ACM.0000000000005605. Epub 2023 Dec 18.
Medical training programs and health care systems collect ever-increasing amounts of educational and clinical data. These data are collected with the primary purpose of supporting either trainee learning or patient care. Well-established principles guide the secondary use of these data for program evaluation and quality improvement initiatives. More recently, however, these clinical and educational data are also increasingly being used to train artificial intelligence (AI) models. The implications of this relatively unique secondary use of data have not been well explored. These models can support the development of sophisticated AI products that can be commercialized. While these products have the potential to support and improve the educational system, there are challenges related to validity, patient and learner consent, and biased or discriminatory outputs. The authors consider the implications of developing AI models and products using educational and clinical data from learners, discuss the uses of these products within medical education, and outline considerations that should guide the appropriate use of data for this purpose. These issues are further explored by examining how they have been navigated in an educational collaborative.
医学培训计划和医疗保健系统收集的教育和临床数据越来越多。这些数据的主要收集目的是支持学员学习或患者护理。既定的原则指导着这些数据的二次使用，以进行计划评估和质量改进举措。然而，最近这些临床和教育数据也越来越多地被用于训练人工智能 (AI) 模型。这种相对独特的数据二次使用的影响尚未得到充分探索。这些模型可以支持开发可以商业化的复杂 AI 产品。虽然这些产品有可能支持和改进教育系统，但在有效性、患者和学习者同意以及有偏差或歧视性的输出方面存在挑战。作者考虑了使用学习者的教育和临床数据开发 AI 模型和产品的影响，讨论了这些产品在医学教育中的用途，并概述了指导出于此目的适当使用数据的考虑因素。通过检查它们在教育协作中的应用方式，进一步探讨了这些问题。
