aceExploring clinician interpretability of AI decision support systems for surgical anatomy recognition
Abstract Artificial intelligence (AI) decision support systems for surgery hold promise but face barriers to adoption, particularly around the interpretability of their outputs. We conducted an international cross-sectional survey of 47 neurosurgeons to evaluate perspectives on literature-derived explanatory techniques for AI-generated anatomical segmentations, using endoscopic pituitary surgery as a high-risk exemplar. Participants ranked certainty scores, certainty maps, saliency maps, scene similarity scores,…
Surgeons ranked and rated certainty scores and certainty maps highest because they match surgical decision-making, suggesting a workable route to more interpretable AI-assisted anatomical navigation.
The study measured surgeons' stated preferences, not outcomes; the effect of these explanations on trust calibration and performance still needs prospective evaluation across surgical domains.
Evidence
- Peer-reviewedScientific Reports2026-10-24
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Truvace Impact Record TRV-2026-2256, v1: “Exploring clinician interpretability of AI decision support systems for surgical anatomy recognition.” Truvace, 2026-10-26. /record/TRV-2026-2256 (accessed at citation time). sha256 6722bbcdbdb479a0…
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