From principle to practice: Developing a digital-age clinical artificial intelligence ethics competence framework...

Area of Science:

• Medical Ethics
• Artificial Intelligence in Healthcare
• Clinical Education

Background:

• Clinical AI integration presents novel ethical challenges for healthcare professionals.
• Traditional medical ethics education may not adequately prepare clinicians for AI-related dilemmas.
• Limited empirical data exists on clinicians' experiences with AI ethics in practice.

Purpose of the Study:

• To explore early-career doctors' perceptions of ethical dilemmas in clinical AI use.
• To identify practical ethical challenges arising from AI in healthcare settings.
• To develop an operationalised competence framework for medical education on AI ethics.

Main Methods:

• Semi-structured interviews with 30 early-career doctors in Singapore (1-5 years post-graduation).
• Purposive sampling ensured diversity across specialties and institutions.
• Reflexive thematic analysis informed by bioethical principles (autonomy, beneficence, non-maleficence, justice).

Main Results:

• Participants reported limited formal AI ethics education.
• Seven key ethical challenges identified: system opacity, dataset bias, data privacy, output contextualisation, hallucinations, accountability, and cognitive offloading.
• Classical bioethical principles were reframed through epistemic, relational, and institutional lenses.

Conclusions:

• Ethical competence in AI-mediated care requires integrated epistemic and relational capacities, extending beyond technical skills.
• The Digital-Age Clinical AI Ethics Competence (DCEC) framework is proposed, comprising epistemic awareness, relational integrity, reflexive accountability, and adaptive professionalism.
• The DCEC framework includes practical learning activities and assessment strategies for curriculum development.