Artificial Intelligence in Neurology and Stroke Education: Current Applications and Future Directions

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.

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