Development and Validation of Artificial Intelligence (AI)-Generated Visual Clinic for Supplementing Clinical Teaching in Resource-Limited Settings: A Proof of Concept Study
DOI:
https://doi.org/10.63808/acde.v2i4.504Keywords:
Artificial intelligence, Clinical teaching, Simulation, Self-efficacy, Stroke rehabilitation, Resource-limited settingsAbstract
Background: Clinical teaching integrates theoretical knowledge with direct patient contact. However, in resource-limited settings, adequate patient contact is often compromised by inadequate clinical facilities, perennial industrial actions by healthcare workers, and limited access to rare or under-referred cases. The aim of this study was to develop an AI-generated visual clinic (a curated catalogue of videos) depicting motor rehabilitation in patients with stroke to help supplement real-world clinical teaching. Methods: An AI platform, Topmediai (https://www.topmediai.com/app/text-to-video/) was used to generate videos on constraint-induced movement therapy (CIMT), motor imagery, sit-to-stand, over-ground gait training and treadmill gait training. The videos were edited and combined into an AI-generated visual clinic using CapCut (https://www.capcut.com/). Students and teachers were asked to rate the AI-generated visual clinic for: 1) Anatomical plausibility, 2) Procedural fidelity, 3) Rehabilitation accuracy, 4) Pedagogical clarity, and 5) Risk of learner misconception on a-5-point Likert scale, where, 1=low, 2= fair, 3=moderate, 4=high, and 5=very high. Results: A complete visual clinic demonstrating CIMT, motor imagery, sit-to-stand, over-ground gait training and treadmill gait training was produced. In addition, majority of the participants (both teachers and students), rated anatomical plausibility, procedural fidelity, rehabilitation accuracy and pedagogical clarity of the AI-generated visual clinic as high or very high; and risk of learner misconception as low or fair. Conclusion: It seems possible to generate clinically relevant videos of disease management using current AI text-to-video tools that can be catalogued into a “visual clinic” and used to support traditional clinical teaching.
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