Educational Impact of Artificial Intelligence‐Navigation Surgery on Anatomical Landmark Recognition in Medical Students

Abstract

ABSTRACT Background We evaluated the educational impact of artificial intelligence (AI)‐navigation surgery for medical students which provides real‐time anatomical landmark recognition during laparoscopic cholecystectomy (LC). Methods Thirty fifth‐year medical students were randomly assigned to three groups: surgeon‐guided ( n = 10), self‐learning ( n = 10), and AI‐learning ( n = 10). Each group annotated anatomical landmarks, extrahepatic bile duct (EHBD), cystic duct (CD), Rouvière's sulcus (RS), the base of liver segment 4 (S4), before and after training. The AI‐learning group received real‐time feedback using a deep learning segmentation model (HyperSeg). Learning outcomes were quantitatively assessed and compared to expert annotations using Dice coefficients, and post‐study questionnaires were analyzed to evaluate understanding of anatomy and surgical procedures. Results The mean Dice coefficients in the surgeon‐guided (0.450 ± 0.025) and AI‐learning groups (0.432 ± 0.038) were significantly higher in comparison to the self‐learning group (0.351 ± 0.057, p = 0.00006). In an itemized analysis, significant improvements were observed in EHBD and RS recognition, but not in CD or S4 recognition. In the post‐study questionnaire assessing anatomical understanding and the ability to comprehend the surgeon's perspective and intentions, the surgeon‐guided group showed significantly better results in comparison to the self‐learning group ( p < 0.001 for each comparison). However, there was no significant difference between the AI‐learning and self‐learning groups. Conclusions AI has the potential to complement surgeon's guidance, reducing faculty burden while maintaining educational quality in surgical education.

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