Health science reports · 2026
AI Agents and the Future of Clinical Judgment in Medical Education: Opportunities, Challenges, and the Need for Human-Centered Integration.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ahmady Soleiman, Kohan Noushin, Monajemi Alireza
Аннотация
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.
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.