Article

Exploring the Interpretability of AI Decision Support Systems for Surgical Anatomy Recognition

2026-06-03

Abstract excerpt

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 explanation techniques for AI-generated anatomical segmentations, using endoscopic pituitary surgery as a high-risk exemplar. Pa...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a56f02bf-05b2-50d8-ba98-4fef18b0eeb8
DOI: 10.64898/2026.06.02.26354729

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Exploring the Interpretability of AI Decision Support Systems for Surgical Anatomy RecognitionDOI 10.64898/2026.06.02.26354729Read

Applications of Artificial Intelligence in Neurosurgical Education: A Scoping ReviewSemantic proximity Read

Who should do as AI say? Only non-task expert physicians benefit from correct explainable AI adviceSemantic proximity Read

The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretationSemantic proximity Read

Bridging the Gap: Explainability Metrics for AI Image-Based Clinical DiagnosticsSemantic proximity Read

Computer Vision for Real-Time Anatomical Navigation in Neurosurgery: First-in-Human Clinical Evaluation and Iterative Development (IDEAL Stage 1)Semantic proximity Read

Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic ReviewSemantic proximity Read

Towards Interpretable AI Second Opinions: Foundation Model Heatmaps in RadiologySemantic proximity Read

Medical Clinical Minds Meet Artificial Intelligence: Italian Physicians’ Knowledge, Attitudes, and Concordance between Italian Physicians and AI-Generated Diagnoses. A National Cross-Sectional StudySemantic proximity Read

Expert Appraisal of AI-Generated Recommendations in Cardiovascular Surgical Decision-Making: A Multisurgeon StudySemantic proximity Read

Synthesizing evidence regarding artificial intelligence generated radiological reports based on medical images: a scoping review protocolSemantic proximity Read

Anatomical Accuracy of Generative AI for Congenital Heart Disease Illustrations: Gemini NanoBanana Versus ChatGPT Models in a Blinded Comparative StudySemantic proximity Read

Awareness and Attitudes of Radiology Staff Toward Artificial Intelligence in Medical Imaging Diagnosis: A Systematic ReviewSemantic proximity Read