ArticleResearch square2026
Explainable Artificial Intelligence (XAI) in Medical Education: A Multi-Modal Framework for Enhancing Human-AI Collaboration.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: While Artificial Intelligence (AI) rapidly advances clinical diagnostic accuracy, the black-box nature of complex algorithms presents critical barriers to pedagogical integration in medical curricula. This study evaluated the efficacy of Explainable AI (XAI) as an interactive pedagogical intervention designed to enhance clinical reasoning and model interpretability during AI-assisted diagnostic training. Methods: A prospective, mixed-methods randomized controlled trial was conducted with 120 third-year medical students. Participants were randomized to either a standard AI instruction group (n = 60) or an XAI-enhanced group (n = 60) utilizing the CerViD-MultiModal framework, which integrated dynamic SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) visualizations into neuroimaging case analyses. Standardized psychometric instruments evaluated AI Literacy (0-100), System Usability Scale (SUS, 0-100), NASA Task Load Index (NASA-TLX, 0-100), and Confidence in AI Interpretation (1-5). Results: The XAI-enhanced group demonstrated statistically significant improvements across all primary outcomes compared to the control group. AI Literacy scores increased markedly by 34.1% (85.0 ± 5.6 vs. 63.4 ± 6.8, p < 0.001, Cohen's d = 3.44). Perceived system usability (SUS) improved by 66.5% (36.3 ± 4.3 vs. 21.8 ± 4.8, p < 0.001, d = 3.15). The XAI intervention significantly attenuated perceived cognitive workload on the NASA-TLX scale by 37.4% (26.5 ± 6.5 vs. 42.3 ± 7.1, p < 0.001, d = -2.31). Furthermore, learner confidence in AI interpretation grew by 30.8% (3.4 ± 0.4 vs. 2.6 ± 0.4, p < 0.001, d = 1.89). Conclusions: Beyond serving as an algorithmic transparency mechanism, XAI operates as a transformative pedagogical instrument in medical training. By demystifying model decision-making processes, the CerViD-MultiModal framework optimizes human-AI collaboration, significantly bolstering diagnostic comprehension and user trust while mitigating cognitive load in clinical learners.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.