Artificial Intelligence in Surgery Revisited: A 2025 Guide to Understanding and Applying AI Models in Clinical Practice

Abstract

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming surgery, moving beyond traditional risk prediction to real-time clinical support and intraoperative assistance. However, successful integration requires clinicians to understand key methodological challenges, including overfitting, data bias, and the "black box" nature of many models, which can obscure interpretability and limit generalizability. Recent advances demonstrate AI's growing ability to process text and audiovisual data to streamline documentation, enhance intraoperative decision-making, and even perform basic operative tasks through robotic automation. This review outlines core ML principles relevant to surgical applications, discusses data modalities and evaluation metrics, and highlights emerging models that exemplify the evolving role of AI in the operating room. As these systems progress from experimental to practical use, understanding both their potential and limitations will be essential to ensure safe, effective, and ethically sound adoption in surgical practice.

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