ArticleHealth science reports2026
AI Agents and the Future of Clinical Judgment in Medical Education: Opportunities, Challenges, and the Need for Human-Centered Integration.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
3 authors.
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Abstract
Background: Artificial intelligence (AI) is actively transforming health professions education by introducing innovative methodologies for learning, simulation, and clinical decision support. The recent emergence of autonomous AI agents-equipped with advanced capabilities like memory, planning, and tool integration-creates unprecedented opportunities for medical training. However, this growing technological autonomy simultaneously introduces critical challenges regarding clinical judgment, professional accountability, and educational equity. Methods: This perspective employs a rigorous critical analysis grounded in health professions education, medical philosophy, and AI ethics literature. It systematically evaluates the pedagogical potentials alongside the epistemological risks associated with deploying autonomous AI agents within clinical training environments. Discussion: While AI agents can effectively drive adaptive learning, scalable simulations, and individualized feedback, unmonitored or inappropriate integration risks undermining core clinical reasoning. Over-reliance on algorithmic suggestions can diminish a learner's independent analytical capabilities and propagate inherent data set biases. Conversely, dismissing these technologies risks depriving learners of vital digital competencies. Consequently, the ultimate educational utility of AI agents is fundamentally dictated by deliberate instructional design and robust governance. Conclusion: Rather than serving as replacements for human expertise, AI agents must be harnessed through a human-centered framework that safeguards clinical reasoning, ethical reflection, and professional accountability. By deploying targeted strategies-such as foundational AI literacy, localized data governance, controlled simulation failures, and modernized evaluations like AI-assisted OSCEs-educational institutions can ensure these technologies reinforce human-driven medical practice.
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Registered trials
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.