Ethics concerns with generative AI in health professions education
The integration of generative AI into health professions education raises numerous ethical considerations that educators, institutions, and students must navigate. These considerations bridge technical, educational, and clinical domains.
Bias and fairness
AI systems are trained on human-generated content that inherently contains societal biases. In healthcare contexts, these biases can perpetuate and amplify existing healthcare disparities. For example:
- Language models may provide different quality responses for medical conditions that predominantly affect underrepresented populations
- AI systems might reinforce existing gender, racial, or socioeconomic biases in healthcare decision-making
- Case examples or clinical scenarios generated by AI might underrepresent diverse patient populations
Health professions educators must teach students to recognize these biases and develop strategies to mitigate their impact on patient care.
Transparency and explainability
Unlike human experts who can explain their reasoning, many AI systems function as “black boxes” where the path to a particular output isn’t transparent. This creates challenges in:
- Validating the accuracy of AI-generated content in educational materials
- Teaching students to appropriately question AI outputs rather than accepting them uncritically
- Establishing responsibility and accountability when AI tools are used in assessment or clinical decision support
Educators should emphasize the importance of explainable AI and teach students to demand transparency in AI systems they may use in their future practice.
Privacy and confidentiality
When integrating AI into health professions education, protecting sensitive information is paramount:
- Student interactions with AI systems may involve sharing personal or confidential information
- Clinical scenarios used in education must be properly de-identified
- Institutional data used to train or fine-tune AI models requires careful governance
Teaching future health professionals about proper data handling in AI contexts prepares them for maintaining patient confidentiality in increasingly digitized healthcare environments.
Professional identity and autonomy
As AI becomes more integrated into healthcare education and practice, questions arise about:
- How AI tools may reshape professional roles and identities
- The appropriate balance between AI assistance and human judgment
- Maintaining critical thinking skills when AI systems offer ready-made answers
Health professions educators must help students develop a strong sense of professional identity and judgment that incorporates AI as a tool rather than a replacement for human expertise and compassion.
Future directions
The field of AI ethics in health professions education continues to evolve. Educators should foster ongoing dialogue about ethical principles that should guide AI development and use, encouraging students to become not just users but informed shapers of how these technologies are deployed in healthcare.