Effectiveness of artificial intelligence-based visualization for surgical anatomy education: A cluster quasirandomized controlled trial.

BACKGROUND

Although surgical techniques have advanced markedly, surgical education for medical students has remained largely traditional. Because of the importance of accurate recognition of intraoperative anatomy, passive observation is insufficient for novice learners. This study evaluated the educational effectiveness of an artificial intelligence-based visualization system that highlights anatomical structures during surgery in real time.

METHODS

We conducted a cluster quasi-randomized controlled trial involving fifth-year medical students assigned to receive either conventional intraoperative teaching (cohort C) or instruction augmented by artificial intelligence-based visualization (cohort A). Each student annotated the pancreatic regions on 6 static laparoscopic or robotic images before and after clinical observation training. Performance was assessed using 3 image segmentation metrics: recall (minimizing false negatives), precision (avoiding false positives), and the Dice coefficient, which integrates recall and precision. The Dice coefficient served as the primary outcome, whereas recall and precision served as secondary outcomes.

RESULTS

Both cohorts showed similar baseline performance and improved across all metrics after training. However, cohort A exhibited a significantly greater improvement in recall than cohort C (ΔRecall: 0.112 vs 0.013, P = .020), indicating enhanced sensitivity in identifying pancreatic regions. Precision did not differ significantly between the groups (P = .746). The Dice coefficient improved more in cohort A than in cohort C (ΔDice: 0.087 vs 0.028), although the between-group difference did not reach statistical significance (P = .097). Scatter plot analysis showed that many cohort A students exhibited increased recall without loss of precision, suggesting improved recognition accuracy.

CONCLUSION

Incorporating artificial intelligence-based visualization into surgical anatomy education may enhance medical students' recognition of intraoperative anatomy, particularly for visually complex structures such as the pancreas.