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Updated: Feb 10, 2026

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Benchmarking Large Language Models Using a Best Evidence Topic Report in a Patient With Early Non-Small Cell Lung

Vivek Chaudhuri1, Alessandro Brunelli2,3, Peter Tcherveniakov2,3

  • 1University of Oxford, Oxford, OX2 6QA, United Kingdom.

Interdisciplinary Cardiovascular and Thoracic Surgery
|February 9, 2026
PubMed
Summary
This summary is machine-generated.

Large Language Models (LLMs) show potential but are not yet reliable for answering clinical questions. A Best Evidence Topic (BET) approach remains the gold standard for evidence-based clinical decision-making.

Keywords:
ChatGPTGeminiGrokMicrosoft CopilotNSCLC—non-small cell lung cancerRATS—robotic-assisted thoracoscopic surgeryVATS—video-assisted thoracoscopic surgery

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Evidence Synthesis

Background:

  • Large Language Models (LLMs) offer conversational AI capabilities.
  • Assessing LLM accuracy for clinical queries is crucial.

Purpose of the Study:

  • To evaluate LLM performance in answering patient-specific clinical questions.
  • To benchmark LLM outputs against a Best Evidence Topic (BET) methodology.

Main Methods:

  • LLMs were queried on comparative outcomes of Robot-assisted Thoracic Surgery (RATS) versus Video-assisted Thoracoscopic Surgery (VATS) lobectomy.
  • A structured BET protocol was employed to identify and synthesize best available evidence for the same clinical questions.
  • Evidence retrieval for BET involved extensive database searching, yielding 12 key papers.

Main Results:

  • LLM responses were rapid but often lacked relevance, contained inaccuracies (hallucinations), and faced access barriers (copyright, paywalls).
  • The BET, guided by human expertise, provided more reliable, nuanced, and tailored information.
  • No significant differences in outcomes were found between RATS and VATS lobectomy for T1cN0M0 NSCLC, except for a shorter length of hospital stay (LOS) with RATS.

Conclusions:

  • Current LLMs are not fully dependable for clinical question answering.
  • A standardized LLM-BET protocol could facilitate comparison of LLM outputs across various clinical scenarios.
  • This protocol can also be used to analyze and compare different LLM models, both current and future.