When to trust the answer: question-aligned semantic nearest neighbor entropy for safer surgical VQA - Takeaways - MDSpire

Determining Reliability in Surgical Visual Question Answering: Utilizing Question-Aligned Semantic Nearest Neighbor Entropy for Enhanced Safety

  • By

  • Luca Carlini

  • Dennis Pierantozzi

  • Mauro Orazio Drago

  • Chiara Lena

  • Cesare Hassan

  • Elena De Momi

  • Danail Stoyanov

  • Sophia Bano

  • Mobarak I. Hoque

  • July 17, 2026

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  • 1

    Surgical Visual Question Answering (VQA) aims to support decision-making in minimally invasive procedures through rapid image interpretation.

  • 2

    Existing surgical VQA systems often fail to recognize and communicate uncertainty, leading to potential safety risks in clinical settings.

  • 3

    QA-SNNE is introduced as a failure-detection score that combines answer consistency with question alignment for improved reliability.

  • 4

    The study evaluates QA-SNNE against existing methods using an out-of-template version of EndoVis18-VQA to assess model robustness.

  • 5

    The methodology emphasizes the importance of question validity in addition to answer consistency for reliable surgical VQA outputs.

Original Source(s)

When to trust the answer: question-aligned semantic nearest neighbor entropy for safer surgical VQA

  • by Luca Carlini, Dennis Pierantozzi, Mauro Orazio Drago, Chiara Lena, Cesare Hassan, Elena De Momi, Danail Stoyanov, Sophia Bano, Mobarak I. Hoque

  • July 17, 2026

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