Exploring clinician interpretability of AI decision support systems for surgical anatomy recognition. Download PDF 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, and nearest-neighbour illustrations, and rated them using a modified Explanation Satisfaction Scale alongside free-text feedback. Certainty-based techniques were consistently ranked and rated highest – valued for aligning with surgical decision-making by conveying confidence (via scores) and anatomical boundaries (via maps). Saliency- and similarity-based methods were judged less clinically relevant and better suited to educational settings.
Therefore, certainty-based techniques appear most acceptable to surgeons for improving the interpretability of AI-assisted anatomical navigation systems, though their impact on trust calibration and performance requires prospective evaluation across surgical domains. Explore related subjects Discover the latest articles and news in related subjects. Computational biology and bioinformatics Health care Mathematics and computing Medical research Funding This work was funded by the EPSRC (EP/Y01958X/1 and EP/W00805X/1).
Medical Disclaimer: This article is for informational purposes only and does not constitute medical advice. Always consult with qualified healthcare professionals for medical decisions and treatment options.
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