ArticleMedical education2026
From principle to practice: Developing a digital-age clinical artificial intelligence ethics competence framework through early-career doctors' experiences.
Article in Medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- From principle to practice: Developing a digital-age clinical artificial intelligence ethics competence framework through early-career doctors' experiences.Medical education · 2026Article
- Is AI replacing faculty? Rethinking faculty roles in medical education.Medical education · 2026Article
- Cognitive reshaping and resurgence of humanness: restructuring the medical education continuum in the era of generative AI.Frontiers in medicine · 2026Review
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Authors and funding
5 authors.
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Abstract
introductionAs artificial intelligence (AI) becomes increasingly embedded in clinical workflows, clinicians encounter ethical challenges that traditional, principle-based medical ethics education may not adequately address. Empirical evidence on clinicians' experiences with AI-related ethics is limited, constraining curricular improvement. This qualitative study explores how early-career doctors in Singapore perceive and negotiate ethical dilemmas arising from clinical AI use and translates findings into an operationalised competence framework for medical education.
methodsBetween April and June 2025, we conducted semi-structured interviews with 30 early-career doctors (1-5 years post-graduation) from nine public healthcare institutions in Singapore. Purposive sampling ensured diversity across specialties, institutions, gender and ethnicity. Interviews explored participants' AI-related ethical challenges in day-to-day practice and their perceptions of ethics training in medical school. Data were analysed using Braun and Clarke's (2022) reflexive thematic analysis, with codes developed iteratively and informed by the four classical bioethical principles as sensitising concepts-autonomy, beneficence, non-maleficence and justice. Interdisciplinary reflexive discussions guided the construction and interpretation of themes.
resultsParticipants reported limited formal AI education. Seven recurring practical ethical challenges were identified: (1) system opacity, (2) dataset bias and generalisability, (3) data privacy and consent in networked environments, (4) insufficient patient-specific contextualisation of outputs, (5) risks of hallucinations, (6) ambiguous accountability and (7) cognitive offloading. These themes reframed classical bioethical principles through epistemic, relational and institutional lenses. DISCUSSION: Ethical competence for AI-mediated care requires integrated epistemic and relational capacities beyond technical literacy or traditional medical ethics. We propose the Digital-Age Clinical AI Ethics Competence (DCEC) framework, comprising four domains of epistemic awareness, relational integrity, reflexive accountability and adaptive professionalism, anchored by ethical digital literacy (EDL). Each domain is operationalised with specific learning activities and assessment strategies such as Objective Structured Clinical Examination (OSCE) stations, reflective portfolios and ethics viva. We discuss implications for curriculum design, faculty development and competency-based assessment.
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