ArticleSurgical endoscopy2025
Artificial intelligence-supported system of surgical anatomy recognition may facilitate the understanding of gastrointestinal surgery for medical students.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence augmented tutoring vs expert instruction on learning simulated general surgical skills: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Artificial intelligence applications in surgical education and training: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Histopathological proof-of-concept validation of AI-assisted peripheral neural track recognition during laparoscopic gastrointestinal surgery.Surgical endoscopy · 2026Article
- Intraoperative real-time recognition of dissectible layers by artificial intelligence is useful in laparoscopic inguinal hernia repair.Surgical endoscopy · 2026Article
- Artificial intelligence-based anatomical recognition improves surgeon decision-making during robotic gastrectomy.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Real-time multicenter semantic segmentation for laparoscopic gastrectomy: development, external validation and operating room deployment of an AI navigation system.Military Medical Research · 2026Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough the high accuracy of artificial intelligence (AI) for recognizing surgical anatomy has been reported, its effective usage remains unclear. In this study, we investigated the utility of AI in surgical education for medical students.
methodsFifth-grade medical students were recruited to investigate the educational utility of EUREKA™. After an introductory lecture, they watched a video of distal gastrectomy with or without the suggestion of the connective tissue and the pancreas by EUREKA™ and then drew dissection lines in still images captured from the video. The distance between the lines drawn by students and the optimal dissection line determined by an expert surgeon was integrated to evaluate how well the students appropriately recognized the dissection line. Students filled out questionnaires after the study. A total of 45 operative video frames from radical gastrectomies performed with three different robotic systems were analyzed. The accuracy of the EUREKA™ recognition of the connective tissue and the pancreas was assessed using Dice and Intersection over Union (IoU) as a measurement tool.
resultsTwelve students participated in the study, and nine students drew dissection lines. All students completed questionnaires. The students could recognize dissection lines more appropriately with the EUREKA™ suggestion, and the deviations between the dissection lines drawn by the students and the optimal dissection lines were significantly reduced. From the questionnaires completed by the students, eight students agreed with the possibility of AI to facilitate their understanding of the operation, and two students agreed with the potential of AI to increase the number of medical students who choose gastrointestinal surgery as their career. There were no differences in the DICE and IoU scores of the connective tissue and the pancreas between the three robotic systems, suggesting the versatility of the EUREKA™ system.
conclusionAI may facilitate students' understanding of surgery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.