Integration of AI literacy into clinical education

The gap

Evidence profile

Sourced from the future work and open questions and inline gaps of the source papers, classified as application gap, spanning 6 journals.

Supporting evidence — 8 representative gaps

• Artificial Intelligence in Radiology: Unlocking New Dimensions of Value (2026) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi

  The successful integration of AI in clinical practice will depend on careful consideration of both its promise and its limitations, as well as ongoing collaboration between technology developers and healthcare professionals.

  application gapfuture workevidence 5/5
  Keywords: successful integration clinical practice depend careful consideration promise limitations well ongoing collaboration technology developers healthcare
• Artificial Intelligence in Medicine. Systems Anatomy, Decision Physiology, Hygiene of Use (2026) · Journal of Gastrointestinal and Liver Diseases · doi

  The challenge is not rapid adoption of AI in medicine, but the sensible integration of these systems into day-to-day practice with sufficient guardrails in place.

  application gapopen questionsevidence 5/5
  Keywords: challenge rapid adoption medicine sensible integration systems practice sufficient guardrails place
• FADOI official position on artificial intelligence in internal medicine (2026) · Italian Journal of Medicine · doi

  The paper proposes a model of AI integration grounded in clinical governance but lacks detailed implementation pathways for translating general AI principles into operational rules applicable to daily practice in complex clinical environments.

  application gapinline gapsevidence 5/5
  Keywords: clinical proposes model integration grounded governance lacks detailed implementation pathways translating general principles operational rules
• FADOI official position on artificial intelligence in internal medicine (2026) · Italian Journal of Medicine · doi

  While the document addresses regulatory and ethical frameworks for AI in internal medicine, there is insufficient discussion of how to manage the cumulative burden of digital health technologies on physicians and patients in real-world settings.

  application gapinline gapsevidence 5/5
  Keywords: document addresses regulatory ethical frameworks internal medicine there insufficient discussion manage cumulative burden digital health
• FADOI official position on artificial intelligence in internal medicine (2026) · Italian Journal of Medicine · doi

  Educational strategies for clinical supervision of AI use are mentioned but not comprehensively addressed in terms of how to implement them across diverse internal medicine settings.

  application gapinline gapsevidence 5/5
  Keywords: educational strategies clinical supervision mentioned comprehensively addressed terms implement them across diverse internal medicine settings
• Stakeholder attitudes toward the ethical impact of use of artificial intelligence in clinical practice: a scoping review (2026) · Health and Technology · doi

  While the review covers AI adoption perspectives in specialized areas (radiology, oncology, cardiology, palliative care, psychiatry), there is no systematic investigation of ethical concerns specific to AI-enabled clinical decision support systems in primary care and community health settings where implementation barriers may differ substantially.

  application gapfuture workevidence 5/5
  Keywords: AI clinical decision support primary care community health ethical concerns implementation
• Artificial intelligence and the future of physicians: replacement or partnership? (2026) · Khyber Medical University Journal · doi

  Healthcare institutions lack standardized frameworks for measuring and rewarding human skills (communication, empathy, cultural sensitivity, shared decision-making, bedside reasoning) as distinct competencies within AI-physician hybrid workflows. Institutional reward mechanisms and performance metrics that balance AI technical indicators with human-centered care attributes remain undefined.

  application gapopen questionsevidence 5/5
  Keywords: shared decision-making empathy physician performance metrics hybrid workflows institutional models
• Emergence of Artificial Intelligence in Multiple Domains of Neurology: A Review (2026) · British Journal of Healthcare and Medical Research · doi

  Integration of AI literacy into clinical education and regulatory frameworks is needed for responsible implementation.

  application gapfuture workevidence 4/5
  Keywords: integration literacy clinical education regulatory frameworks needed responsible implementation

Questions about this gap