Volume 5, Issue 3 (2025) – 14 articles
Cover Picture: Artificial intelligence (AI) integration into surgical practice has advanced intraoperative precision, complication prediction, and procedural efficiency. While AI has demonstrated advancements in colorectal, cardiac, and other laparoscopic procedures, its application in inguinal hernia repair (IHR), one of the most commonly performed surgeries, remains underexplored. AI models demonstrate potential in real-time recognition of surgical phases, anatomical structures, and instruments, particularly in transabdominal preperitoneal (TAPP), total extraperitoneal (TEP), and robotic inguinal hernia repair (RIHR). This systematic review evaluates the accuracy, applicability, and clinical impact of AI-based systems in real-time surgical phase recognition during IHR. AI and ML models demonstrate significant potential in achieving real-time surgical phase recognition during minimally invasive IHR. Despite promising accuracies, challenges such as heterogeneity in model performance, reliance on annotated datasets, and the need for real-time validation persist. Standardized benchmarks, multicenter studies, and hardware advancements will be essential to fully integrate AI into surgical workflows, improving surgical training, technical performance, and patient outcomes.
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