Artificial Intelligence for Colorectal Surgeons-Part I: Foundations and Clinical and Educational Applications.

BACKGROUND

Artificial intelligence is rapidly transforming colorectal surgery through applications spanning screening to postoperative care. This 2-part review provides a comprehensive framework for understanding and implementing artificial intelligence technologies in surgical practice.

OBJECTIVE

Part I analyzes fundamental artificial intelligence concepts, clinical applications across the surgical continuum, and educational considerations for colorectal surgeons.

METHODS

Comprehensive literature review focusing on artificial intelligence applications in colorectal surgery, evaluating current evidence for clinical implementation and training approaches.

RESULTS

Artificial intelligence demonstrates measurable clinical impact across multiple domains. In screening, artificial intelligence-enhanced colonoscopy increases adenoma detection rates from 40.4% to 54.8% ( p < 0.001) in multicenter trials, with cross-validation on colonoscopy images demonstrating 96.4% polyp detection accuracy. Diagnostic applications include microsatellite instability prediction from histology and automated MRI tumor segmentation. Preoperatively, machine learning models predict mortality and morbidity, outperforming traditional risk measures such as the American Society of Anesthesiologists classification and the American College of Surgeon-National Surgical Quality Improvement Program. Intraoperatively, artificial intelligence has demonstrated early feasibility for real-time tissue plane, artificial intelligence-based automated perfusion assessment can predict anastomotic viability, and artificial intelligence can enable identification of critical structures such as pelvic nerves. Postoperatively, artificial intelligence is starting to detect complications hours before clinical diagnosis, reducing documentation time. Educational applications include artificial intelligence-enhanced simulation platforms, objective video-based performance assessment, and integration with virtual/augmented reality for immersive training experiences.

CONCLUSIONS

Artificial intelligence technologies have achieved clinical validation for specific colorectal surgery applications, with several technologies receiving regulatory approval. Successful integration requires understanding fundamental concepts, appropriate clinical application, and structured training approaches. Part II of this series analyzes research applications, implementation resources, challenges, and future directions that will further transform surgical practice.