Q: What are the main AI risks in medical training? A: AI risks in medical training include automation bias, deskilling, cognitive offloading and outsourcing of reasoning that can erode critical thinking and memory. Other major concerns are reinforcement of existing biases and privacy or data governance breaches that could harm patients. Q: How can overreliance on AI weaken clinical skills? A: Overreliance can lead trainees to accept suggested diagnoses without forming full differentials, narrowing their clinical reasoning and masking rare but serious causes. This pattern of cognitive offloading and reduced practice undermines memory retention and hands-on skills needed in emergencies. Q: What institutional policies can reduce harm from AI use in education and practice? A: Institutions should set clear boundaries defining allowed tools and red lines (for example, no patient uploads to public apps), require logging of AI use, and create approval pathways for new tools with security checks. They should also mandate enterprise-grade deployments, de-identification procedures and strict consent rules to address AI risks in medical training. Q: How should assessments be redesigned to protect bedside and reasoning skills? A: Assessments should include AI-free stations such as supervised bedside interviews, physical exams and team scenarios, alongside oral exams and written differentials that test clinical reasoning. Adding a formal AI competency focused on prompt design, verification, bias spotting and error reporting helps ensure responsible use. Q: In what ways can AI be used safely in medical education without undermining learning? A: AI can assist with imaging triage, draft documentation for clinician editing, create simulation cases for reasoning drills, and generate quiz items that require learners to explain answers. Used with human verification and reflective steps, these applications speed lower-risk tasks while keeping humans responsible for high-stakes decisions. Q: What curriculum changes are recommended to teach safe AI use? A: Curricula should teach how LLMs work, where they fail and why they hallucinate, plus source-checking, structured skepticism and bias labs comparing outputs across diverse patient vignettes. Training should build data literacy and make the evaluation of AI a formal competency so learners can design, test and safely incorporate tools. Q: How can patient privacy be protected when AI tools are used in training? A: Protect privacy by using enterprise or compliant cloud solutions with audit logs and role-based controls, and by applying automated and manual de-identification before data reuse. Institutions should set strict consent rules, limit training data use and train staff on safe prompting and redaction to prevent breaches. Q: How can schools and hospitals measure whether mitigation strategies are working? A: Measure progress by tracking diagnostic accuracy, time to correct diagnosis, and the breadth and justification of differentials in student notes, plus unannounced AI-free quizzes to assess retention. Also monitor patient satisfaction and log AI-related near-misses with root cause analysis to inform policy updates.