Artificial intelligence–assisted shared decision-making training for medical students transitioning to residency

Abstract Problem Although the use of artificial intelligence (AI) as a diagnostic aid is increasing in clinical practice, medical education provides little training on how to incorporate AI-generated information into diagnosis and use it effectively in shared decision-making (SDM) with patients. Approach The authors developed and piloted a simulation-based course to train AI-assisted SDM to final-year medical students preparing for residency. Conducted between June and October 2023, the course combined online prelearning with onsite simulations using clinically approved AI tools (Lunit INSIGHT CXR, version 3.1.4.1 and MMG, version 1.1.4.3; Lunit Inc., Seoul, South Korea; used November 16 and 27, 2023). Scenarios portrayed asymptomatic patients with incidental findings (eg, pulmonary nodules, breast microcalcifications). Students engaged in two 12-minute simulated patient encounters featuring SDM with 2 management options. Sessions concluded with simulated patient–written feedback and expert-facilitated debriefing. Twenty-seven students from 3 medical schools participated. Outcomes Program evaluation showed significant improvements in participants’ comprehension and confidence in SDM (t = 6.51 and t = 7.56, P .001, respectively) and AI-assisted SDM (t = 5.72 and t = 5.80, P .001, respectively). Students found AI tools helpful for facilitating SDM and patient communication. Thematic analysis of interviews highlighted strengths, such as structured course design and reflective debriefing. Participants noted that prior education focused on diagnostic algorithms, whereas this course emphasized patient communication and preference-based decisions. They found AI tools useful for diagnosis and supporting discussion with patients through visual outputs. However, they identified limitations, including their own clinical knowledge gaps and lack of explainability in AI tool shortage. They suggested integrating SDM and AI-assisted diagnosis training into formal curricula to better prepare students for clinical practice. Next Steps Future efforts should focus on integrating this course into undergraduate curricula or transition training programs to provide experiential learning opportunities in AI-assisted clinical practice.