Medical training increasingly integrates AI-driven diagnostic support tools and predictive analytics into clinical rotations, aiming to enhance decision-making speed and expose students to the technological future of healthcare. However, because these proprietary algorithms are often trained on historical datasets that may codify long-standing socio-economic or racial health disparities, they risk "automating" bias under the guise of objective, data-driven medical science. How are you restructuring clinical case studies or simulation exercises to ensure that students develop the professional skepticism necessary to audit algorithmic recommendations against their own direct patient observations and clinical judgment?
