Artificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice.

This review explores the current state of eversion robotics in the context of colonoscopy, given the need for less invasive, more patient-friendly screening technologies. Conventional colonoscopy often leads to discomfort and patient reluctance, contributing to delayed diagnoses and high colorectal cancer mortality rates. Eversion robots, also known as vine robots or soft growing robots are soft, pressure-driven devices that extend by everting from the tip whilst offering a promising option by enabling frictionless advancement and potentially pain-free procedures. We examine the key challenges and opportunities in adapting eversion robots for clinical endoscopic use, focusing on material selection, actuation, steering, and payload delivery. From the literature, thermoplastic polyurethane emerges as the most viable material for the robot’s sleeve due to its airtightness, biocompatibility, suitability for heat or ultrasonic welding, and availability in highly flexible thin layers. Tip-steering mechanisms are identified as the most effective strategies for navigation, allowing high flexibility without increasing the wall thickness of the robot, as required in alternative approaches using distributed actuation mechanisms. The review also evaluates strategies for integrating functional tools at the tip of the robot, concluding that cap-free designs provide superior adaptability to the varying colon diameter, preserve compressibility, and keep tip friction to a minimum, unlike cap-based payload delivery methods. By consolidating current research and identifying pathways for innovation, this review supports the development of eversion soft robots as a next-generation solution for minimally invasive colorectal diagnostics and therapy.