Progress in AI adoption in medicine will depend on balanced and responsible adoption rather than novelty and immediate integration, requiring further work on implementation frameworks.

The gap

Consensus across the literature

Supporting evidence — 5 representative gaps

• Artificial Intelligence in Medicine. Systems Anatomy, Decision Physiology, Hygiene of Use (2026) · doi

  Progress in AI adoption in medicine will depend on balanced and responsible adoption rather than novelty and immediate integration, requiring further work on implementation frameworks.

  Keywords: adoption progress medicine depend balanced responsible rather novelty immediate integration requiring further implementation frameworks
• FADOI official position on artificial intelligence in internal medicine (2026) · doi

  The paper references the need for bridging the gap between AI developers and implementers in health AI, but does not provide specific strategies or frameworks for achieving this integration in clinical practice.

  Keywords: references need bridging developers implementers health provide specific strategies frameworks achieving integration clinical practice
• Application of artificial intelligence in pediatric dentistry: a systematic review (2026) · doi

  AI is not yet widely applied in clinical practice due to limited training data, absence of methodology and standards for program development, unconfirmed value and usefulness of AI solutions, and underdeveloped issues of ethics and accountability for decisions made.

  Keywords: widely applied clinical practice limited training absence methodology standards program development unconfirmed value usefulness solutions
• AI in medicine and traditional medicine - opportunities for healthcare transformation (2025) · doi

  The integration of traditional Chinese medicine with artificial intelligence has been surveyed for attitudes and perceptions from medical staff, but specific implementation protocols for TCM-AI systems across different clinical departments and patient populations remain underdeveloped and require standardized integration frameworks.

  Keywords: traditional Chinese medicine artificial intelligence integration clinical implementation standardization
• Artificial intelligence and the future of physicians: replacement or partnership? (2026) · doi

  Existing regulatory and accountability frameworks for AI deployment in clinical settings lack clearly operationalized responsibility structures, audit mechanisms, and bias mitigation strategies specific to AI-physician hybrid decision-making contexts. Concrete governance models defining human oversight requirements and transparency specifications for clinical AI systems need development.

  Keywords: AI governance accountability framework bias mitigation clinical oversight transparency requirements