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

Share

Clinical Report: Determining Reliability in Surgical Visual Question Answering

Background

Surgical visual question answering (VQA) is critical in minimally invasive procedures, where rapid interpretation of complex visual scenes is necessary. Current VQA systems often lack mechanisms to communicate uncertainty and may overestimate their robustness to clinical language variation.

Data Highlights

No numerical or trial data was provided in the source material.

Key Findings

  • Current surgical VQA systems primarily optimize for utility rather than safety.
  • Existing methods often fail to recognize and communicate uncertainty effectively.
  • QA-SNNE is proposed as an operational failure-detection score that assesses answer trustworthiness.
  • Semantic Nearest Neighbor Entropy (SNNE) measures answer-set reliability but does not ensure question validity.
  • Uncertainty estimation should capture both answer consistency and the relevance to the conditioning question.

Clinical Implications

The development of QA-SNNE is discussed as a method for assessing answer trustworthiness in surgical VQA systems.

Conclusion

QA-SNNE is introduced as a method for improving the reliability of surgical VQA systems.

Related Resources & Content

  1. Frontiers in Medicine, 2026 -- Scene graph-guided uncertainty decomposition improves confidence calibration in surgical visual question answering
  2. Int. Journal of Computer Assisted Radiology and Surgery, 2026 -- SurgViVQA: temporally grounded video question answering for surgical scene understanding
  3. Enhancing Surgical Video Question Answering through Scene Graph Insights
  4. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions | FDA
  5. AORN Releases Evidence-Based AI Guideline for Safer Surgical Care | AORN
  6. Integrating Advanced AI with Neurosurgical Expertise: A Conceptual Study on Vagus Nerve Stimulation in Neuromodulation
  7. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions | FDA
  8. AORN Releases Evidence-Based AI Guideline for Safer Surgical Care | AORN
  9. The role of artificial intelligence in adenoma detection during colonoscopy: a systematic review and meta-analysis of randomized controlled trials-Artificial intelligence and adenoma detection - PubMed

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

Related Content

Can Sugammadex Lower Postsurgical Pulmonary Complications?

A large international randomized trial found fewer postoperative pulmonary complications with sugammadex vs. neostigmine following major abdominal or thoracic surgery, although the difference was driven primarily by reduced atelectasis.

A More Direct Route to Intervention: Baptist Health Expands Surgical Pathways for Drug-Resistant Epilepsy

For patients who continue to have seizures despite appropriate anti-seizure medication therapy, timely referral for surgical evaluation can change the trajectory of care.

  • by

  • July 17, 2026

  • 6 min
An interpretable machine-learning model for early prediction of acute kidney injury in polytrauma patients