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Evaluating Large-Language Models Against Providers on Surgical Diagnostic Reasoning Tasks.

Taj Keshav1, David Chow2, Tiffany Kippenberger2

  • 1Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

The Journal of Surgical Research
|April 16, 2026
PubMed
Summary
This summary is machine-generated.

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General surgery residents demonstrated superior accuracy in differential diagnoses compared to large language models (LLMs). LLMs showed higher internal consensus, suggesting context-specific utility in surgical education.

Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Surgical Training

Background:

  • Large language models (LLMs) show promise in surgery-related tasks but their diagnostic reasoning utility is unclear.
  • Assessing LLM accuracy against surgical residents in differential diagnosis construction is crucial.

Purpose of the Study:

  • To evaluate the accuracy and internal consensus of surgical residents versus GPT-3.5 and GPT-4 in generating differential diagnoses.
  • To compare LLM diagnostic performance with that of junior residents, senior residents, and attending surgeons.

Main Methods:

  • Eleven general surgery residents developed differential diagnoses for 10 case vignettes.
  • GPT-3.5 and GPT-4 were queried 30 times per vignette.
  • Attending surgeons' diagnoses were combined with resident diagnoses using a Borda count to create a gold standard.
Keywords:
Artificial intelligenceDiagnostic reasoningLarge language modelResident performanceSurgical education

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Main Results:

  • Residents outperformed GPT-4 and GPT-3.5 in diagnostic accuracy.
  • LLMs exhibited greater internal consensus than residents.
  • Performance varied by case ambiguity and specialty; residents excelled in ambiguous gastrointestinal cases, while LLMs led in non-gastrointestinal cases.

Conclusions:

  • LLM utility in surgical education is context-dependent.
  • LLMs may be most beneficial for less experienced residents facing unfamiliar pathologies.
  • Integrating LLMs requires careful consideration of their strengths and weaknesses in diagnostic reasoning.