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Postgraduate Education in Artificial Intelligence: A Proposal from the Artificial Intelligence Commission of the
João Frutuoso1, Ana Rita Maria2, Helena Donato3
1Comissão para a Inteligência Artificial. Ordem dos Médicos. Lisboa. & Serviço de Medicina Intensiva. Unidade Local de Saúde de Lisboa Ocidental. Lisboa. & NOVA Medical School. Lisboa. Portugal.
Abstract:
Artificial intelligence (AI) is entering clinical practice through decision-support systems, predictive tools, generative models, and clinical documentation solutions. Since February 2025, the European AI Act has required providers and deployers of AI systems to ensure that staff and other people operating or using such systems on their behalf have an adequate level of AI literacy. This creates a new educational requirement: physicians need to acquire minimum competencies to use, appraise, supervise, and reject AI outputs when appropriate. This state-of-the-art review examined AI competencies relevant to postgraduate medical training, focusing on literature published since 2022. The reviewed literature suggests thematic convergence around six core domains: foundational AI literacy, critical appraisal of AI tools, safe clinical application, ethics/law/governance, patient communication, and health data literacy. Other domains, such as institutional implementation, multidisciplinary collaboration, and AI leadership, appear as intermediate or advanced competencies. We propose a three-tier curricular organization: baseline competencies for all physicians; proficient competencies for physicians involved in local appraisal and implementation of AI systems; and advanced competencies for clinician-scientists, institutional leaders, and professionals with formal responsibilities in AI governance. In the Portuguese context, this structure may support the development of transversal training for residents and specialists, aligned with technological change, European regulatory requirements, and the need to preserve human clinical responsibility. This proposal should be understood as a conceptual basis for multidisciplinary consensus validation.