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Area of Science:

  • Surgical Technology
  • Medical Artificial Intelligence
  • Machine Learning in Medicine

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly impacting surgical practice.
  • Current applications extend beyond risk prediction to real-time clinical support and intraoperative assistance.
  • Successful integration necessitates addressing challenges like overfitting, data bias, and model interpretability.

Purpose of the Study:

  • To review core machine learning (ML) principles relevant to surgical applications.
  • To discuss essential data modalities and evaluation metrics for AI in surgery.
  • To highlight emerging AI models and their role in the operating room.

Main Methods:

  • Review of current literature on AI and ML in surgical settings.
  • Analysis of methodological challenges including overfitting, data bias, and interpretability.
  • Examination of AI applications in processing text, audiovisual data, and robotic automation.

Main Results:

  • AI and ML demonstrate capabilities in streamlining documentation, enhancing decision-making, and automating basic surgical tasks.
  • Advances in AI enable processing diverse data modalities for surgical insights.
  • Emerging models showcase the expanding utility of AI in operating room environments.

Conclusions:

  • Understanding the potential and limitations of AI/ML is crucial for safe and effective surgical adoption.
  • Ethical considerations are paramount as AI systems transition from experimental to practical use.
  • Continued research and education are vital for clinicians to leverage AI in surgery responsibly.