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Area of Science:

  • Surgical Innovation
  • Clinical Decision Support Systems
  • Artificial Intelligence in Medicine

Background:

  • Current artificial intelligence (AI) research in surgery focuses on decision support models.
  • Despite numerous development and validation studies, successful bedside implementation of AI models remains limited.
  • Existing guidelines (TRIPOD-AI, DECIDE-AI) address development/validation and staged implementation separately.

Purpose of the Study:

  • To propose integrating implementation considerations from the outset of AI model development.
  • To provide guidance on designing trustworthy and actionable AI clinical decision support models.
  • To address the challenges of bridging the database-to-bedside gap for AI implementation.

Main Methods:

  • Review of lessons learned from high-performing AI decision support models with implementation challenges.
  • Discussion of study design considerations for future AI model development.
  • Analysis of factors contributing to successful AI implementation in clinical practice.

Main Results:

  • Few AI models achieve their intended goals post-implementation.
  • Implementation challenges often arise despite robust development and validation.
  • Proactive consideration of implementation is crucial for AI success.

Conclusions:

  • Future AI clinical decision support models require a paradigm shift, integrating implementation planning before development.
  • Designing for trustworthiness and actionability is key to successful AI adoption in surgery.
  • Addressing the database-to-bedside gap necessitates a holistic approach from inception to deployment.