Abstract
Artificial intelligence (AI) is transforming neurology and stroke education through
applications like automated feedback, adaptive simulations, and enhanced exposure
to critical events. This narrative review explores foundational AI concepts, current
educational uses in professional and patient training, virtual patients, tutoring
tools, and personalized assessment. We evaluate the growing evidence for AI's effectiveness
in improving knowledge, skills, and learner engagement, alongside implementation strategies.
Key challenges include accuracy, bias, ethics, resource gaps, and potential skill
decay. Conclusions emphasize that while AI shows promise for personalized learning
and objective assessment, realizing its potential requires addressing barriers like
cost-effectiveness, faculty readiness, and an evolving curriculum. Thoughtful integration
requires rigorous validation, ethical standards, and further research into long-term
outcomes. Ultimately, AI can complement traditional mentorship, preparing neurologists
for data-driven practice.
Keywords
artificial intelligence - medical education - neurology training - clinical simulation
- stroke assessment
