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Unveiling the Algorithm: The Role of Explainable Artificial Intelligence in Modern Surgery.

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Explainable Artificial Intelligence (XAI) is crucial for safe surgical AI integration. This review highlights XAI

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Area of Science:

  • Surgical innovation
  • Medical Artificial Intelligence
  • Explainable AI

Background:

  • Artificial Intelligence (AI) is transforming surgical care through enhanced diagnosis, prediction, and decision-making.
  • Challenges include AI model opacity, bias, and data sharing, hindering clinical trust and regulatory approval.
  • Explainable Artificial Intelligence (XAI) is emerging to address these gaps, facilitating AI adoption in data-driven surgery.

Purpose of the Study:

  • To review current applications of XAI in surgical contexts.
  • To highlight the absence of XAI in Generative AI tools like ChatGPT.
  • To emphasize XAI as a prerequisite for responsible surgical AI innovation.

Main Methods:

  • Literature review of XAI applications in surgery.
  • Analysis of XAI's role in preoperative assessment, surgical planning, intraoperative guidance, and postoperative monitoring.
  • Identification of challenges including model bias, overfitting, and user interface design.

Main Results:

  • XAI applications are reviewed across the surgical workflow.
  • Generative AI tools currently lack XAI mechanisms.
  • Key challenges for XAI integration include bias, overfitting, and UI design.

Conclusions:

  • XAI is essential for surgeons to interpret, validate, and trust AI tools in surgery.
  • Responsible innovation in surgical AI necessitates XAI implementation.
  • Overcoming challenges in bias, overfitting, and design is key for integrating XAI into surgical practice for transparent and human-centered AI.