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Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning

Paper · 2026 · Flavio Merenda, Jose Manuel Gomez-Perez and German Rigau

Summary

This paper asks whether large language models solve difficult clinical questions through genuine multi-step reasoning or mainly through pattern matching. The authors draw on medical problem-solving theory and test 77 LLMs, ranging from 1B-parameter open models to frontier systems and covering general, medical and reasoning-oriented fine-tuning as well as several prompting strategies. The evaluation spans three QA benchmarks, each probing a different skill. MedAgentsBench covers hard knowledge-and-reasoning questions, MedARC-QA tests susceptibility to cognitive bias (the Einstellung effect), and MetaMedQA measures metacognition. The authors also hand-label a subset of questions by the inference type physicians rely on: abduction, deduction or induction. This gives a fine-grained view that earlier medical evaluations have largely lacked. Scores are generally weak. Many open models fail to beat a random baseline, and neither specialized tuning nor advanced prompting rescues smaller ones. Larger frontier models, especially those tuned for reasoning, improve only modestly, and induction is the weakest skill for every model. The study argues that dedicated training data aligned with physician reasoning strategies is needed before LLMs can be trusted in clinical decision support.

Key points

  • The study evaluates 77 LLMs, from about 1B parameters up to frontier models accessed through APIs, under different fine-tuning types and prompting strategies. Code and data are available at github.com/expertailab/Can-LLMs-Reason-Like-Doctors.
  • The benchmarks are as follows:
    • MedAgentsBench gathers difficult subsets from eight datasets, including MedQA, MedMCQA, MedBullets, PubMedQA, MMLU, MMLU-Pro, MedExQA and MedXpert. It keeps only questions that fewer than half of the systems answered correctly.
    • MedARC-QA uses adversarial answer options.
    • MetaMedQA includes malformed and fictional questions and was downsampled to match the MedQA subset.
  • A new manually annotated subset sorts questions by abduction, deduction and induction. Models fall short on all three types, and induction is the weakest.
  • Reasoning-focused fine-tuning helps more than medical tuning or prompting, but the gains appear mainly in frontier models.
  • Data contamination was checked with the MELD framework. The authors note several limitations: the data is English-only, the tasks are static multiple-choice questions, and MELD's recall is unknown.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksMedicine · 241 worksHealth Informatics · 184 worksHealth Professions · 69 worksHealth Information Management · 50 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Flavio Merenda, Jose Manuel Gomez-Perez and German Rigau. 2026. Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.127

@inproceedings{merenda2026can,
  title = {{Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning}},
  author = {Flavio Merenda, Jose Manuel Gomez-Perez and German Rigau},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.127},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.127}
}

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