Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications

BACKGROUND Artificial intelligence (AI) is playing an increasingly significant role in predicting outcomes of gastrointestinal (GI) surgeries, improving preoperative risk assessment and post-surgical decision-making. AI models, particularly those based on machine learning, have demonstrated potential in predicting surgical complications and recovery trajectories. AIM 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. METHODS 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. RESULTS 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. CONCLUSION 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.