Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.108198
Revised: April 12, 2025
Accepted: May 13, 2025
Published online: June 8, 2025
Processing time: 60 Days and 2.3 Hours
Artificial intelligence (AI) is playing an increasingly significant role in predicting outcomes of gastrointestinal (GI) surgeries, improving preoperative risk assess
To evaluate the role of AI in predicting outcomes for GI surgeries, focusing on its efficacy in enhancing surgical planning, predicting complications, and optimizing post-operative care.
A systematic review of studies published up to March 2025 was conducted across databases such as PubMed, Scopus, and Web of Science. Studies were included if they utilized AI models for predicting surgical outcomes, including morbidity, mortality, and recovery. Data were extracted on the AI techniques, performance metrics, and clinical applicability.
Machine learning models demonstrated significantly better performance than logistic regression models, with an area under the curve difference of 0.07 (95%CI: 0.04–0.09; P < 0.001). Models focusing on variables such as patient demographics, nutritional status, and surgical specifics have shown improved accuracy. AI’s ability to integrate multifaceted data sources, such as imaging and genomics, contributes to its superior predictive power. AI has improved the early detection of gastric cancer, achieving 95% sensitivity in real-world settings.
AI has the potential to transform GI surgical practices by offering more accurate and personalized predictions of surgical outcomes. However, challenges related to data quality, model transparency, and clinical integration remain.
Core Tip: Artificial intelligence (AI) is revolutionizing gastrointestinal surgery by enhancing predictive capabilities for surgical outcomes. Machine learning models, which process diverse data such as patient demographics, imaging, and genomics, outperform traditional methods in predicting complications, mortality, and recovery trajectories. These models enable more personalized preoperative planning and postoperative care. AI integration in surgical practice improves decision-making and enhances patient outcomes, though challenges persist, including data quality, model transparency, and ethical concerns. Future advancements lie in improving model interpretability, expanding data sources, and integrating real-time AI-driven predictions into clinical workflows to optimize patient care and resource management.