900 Section - Innovating PM&R Education in Low- and Middle-Income Countries Through Virtual Lectures: Trainee Perceptions of Real-Time AI-Generated Case Scenarios

Objectives : Physical Medicine and Rehabilitation (PM&R) training is limited in many low- and middle-income countries, contributing to workforce shortages amid rising disability burdens. Traditional models, reliant on in-person teaching and external resources, often provide insufficient exposure to diverse patient scenarios. Artificial Intelligence (AI) offers an opportunity to expand access, personalize learning, and simulate a wide range of clinical cases. This study evaluated the feasibility, acceptability, and educational impact of AI-integrated training for PM&R physicians in low-resource settings.

Design: Twenty-five PM&R fellows from multiple African countries participated in an AI-enhanced educational intervention. Baseline surveys assessed AI familiarity, perceived utility, and concerns including accuracy, cultural relevance, and data privacy. The intervention included a foundational session on AI in medicine followed by faculty-led, real-time AI-generated case scenarios, covering topics such as amputee care. Post-session surveys evaluated changes in knowledge, confidence, and attitudes toward AI use. A continuing series of workshops further explored localized content adaptation, interactive simulations, ethics, and blended mentorship.

Results: Pre-intervention, 84% of fellows reported moderate familiarity with AI, yet 60% had only basic knowledge of its medical applications, and confidence in clinical use was low-to-intermediate. Interest in AI integration was high (88%). Primary concerns included accuracy (88%), lack of culturally relevant content (56%), and data privacy (36%). Following the AI case sessions, participants reported increased confidence in using AI, improved recognition of its value for self-directed learning, and a greater likelihood of incorporating AI tools into practice. Ongoing workshops reinforced contextual relevance, ethical awareness, and engagement.

Conclusion: PM&R fellows in low-resource settings demonstrate strong receptivity to AI-enhanced education. Hands-on exposure to real-time AI-generated cases improves confidence, promotes self-directed learning, and contextualizes local clinical realities. Addressing accuracy, cultural relevance, and data governance concerns provides an ethically grounded, scalable model for AI integration into specialty training in resource-constrained environments.