Using Artificial Intelligence for Distance Medical Education via Zoom: A Case-Based Learning Model

Abstract

Artificial intelligence (AI) presents new opportunities for enhancing distance medical education, particularly through case-based learning formats conducted via virtual platforms like Zoom. This article proposes a structured model for integrating AI-generated clinical cases into remote teaching and learning. AI tools can simulate complex patient scenarios with realistic histories, examinations, laboratory findings, and imaging, enabling students to practice diagnostic reasoning and collaborative problem-solving. A pilot implementation with third- and fourth-year medical students demonstrated significant benefits: 92% found the AI cases realistic, 85% felt more confident in clinical reasoning, and 88% reported greater engagement compared to traditional online lectures. Students appreciated the authenticity and unpredictability of the cases, which encouraged them to think clinically rather than memorise facts. Instructors observed stronger participation and peer-to-peer dialogue. While challenges such as content validation, ethical transparency, and technological dependence persist, the model represents a cost-effective and scalable approach for enhancing remote clinical education. Integration with virtual and augmented reality platforms may further expand its potential.