Artificial Intelligence in Surgical Gastroenterology: From Predictive Models to Intraoperative Guidance

Background: Artificial intelligence (AI) is transforming surgical gastroenterology by enabling data-driven precision across the preoperative, intraoperative and postoperative continuum. This review aims to synthesise recent advances and evaluate the clinical applicability of AI-powered platforms in gastrointestinal (GI) surgery. Objectives: To provide a comprehensive overview of AI integration in surgical gastroenterology between 2015 and 2025, focusing on predictive analytics, intraoperative guidance, postoperative surveillance, implementation challenges and the Indian context. Methods: A narrative review was conducted using the PubMed, Scopus and Web of Science databases. Studies published between January 2015 and May 2025 were included. AI domains covered include machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision and augmented reality (AR). Only peer-reviewed, English-language articles relevant to surgical GI practice were selected. Results: AI models have demonstrated superior accuracy over traditional scoring systems in predicting anastomotic leaks (ALs), readmissions and postoperative liver failure. Intraoperatively, real-time computer vision platforms enable anatomical recognition, tool tracking and perfusion analysis. Postoperative monitoring systems, such as FluidAI and MySurgeryRisk, provide early complication alerts through multimodal data integration. Despite the promise, challenges persist, including data heterogeneity, limited external validation, algorithmic bias and regulatory ambiguity. A tailored roadmap for Indian healthcare outlines priorities in dataset development, ethics frameworks and capacity building. Conclusion: AI is poised to augment decision-making and improve outcomes in surgical gastroenterology. Its successful adoption depends on equitable data access, transparent algorithms and surgeon-led innovation, particularly in resource-diverse settings such as India.