Reasoning with large language models for medical question answering
Open accessAbstract
Objectives: To investigate approaches of reasoning with large language models (LLMs) and to propose a new prompting approach, ensemble reasoning, to improve medical question answering performance with refined reasoning and reduced inconsistency. Materials and Methods: We used multiple choice questions from the USMLE Sample Exam question files on 2 closed-source commercial and 1 open-source clinical LLM to evaluate our proposed approach ensemble reasoning. Results: On GPT-3.5 turbo and Med42-70B, our proposed ensemble reasoning approach outperformed zero-shot chain-of-thought with self-consistency on Steps 1, 2, and 3 questions (+3.44%, +4.00%, and +2.54%) and (2.3%, 5.00%, and 4.15%), respectively. With GPT-4 turbo, there were mixed results with ensemble reasoning again outperforming zero-shot chain-of-thought with self-consistency on Step 1 questions (+1.15%). In all cases, the results demonstrated improved consistency of responses with our approach. A qualitative analysis of the reasoning from the model demonstrated that the ensemble reasoning approach produces correct and helpful reasoning. Conclusion: The proposed iterative ensemble reasoning has the potential to improve the performance of LLMs in medical question answering tasks, particularly with the less powerful LLMs like GPT-3.5 turbo and Med42-70B, which may suggest that this is a promising approach for LLMs with lower capabilities. Additionally, the findings show that our approach helps to refine the reasoning generated by the LLM and thereby improve consistency even with the more powerful GPT-4 turbo. We also identify the potential and need for human-artificial intelligence teaming to improve the reasoning beyond the limits of the model.
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Extensive prior work has benchmarked LLMs against medical board exams and sophisticated research-based questions.
“Extensive prior work has focused on advanced medical question answering, in which LLMs are benchmarked against medical board exams and sophisticated research-based questions13–17.”
From Large language models provide unsafe answers to patient-posed medical questions (2026) · cites this paper as [15]
The 'LLMs in healthcare' section describes multiple prior studies benchmarking LLMs on medical board-exam questions, including CIBE, AKT, NBME and USMLE questions. The passages do not establish that prior work also benchmarked LLMs on sophisticated research-based questions. (LLM, conf=0.85)
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OLIVE: Output-Level Iterative Verification Engine
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Ran, X., Yin, H., Yang, Y., Duan, S., Luo, X., Liu, Y., & Liao, M. (2026) · JMIR Medical Education · doi:10.2196/111146
- Authors
- Lucas, M. M., Yang, J., Pomeroy, J. K., & Yang, C. C.
- Affiliations
- Drexel University (US)
- University of Maryland, College Park (US)
- University of Pennsylvania Health System (US)
- Corresponding author
- Yang, Christopher C
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-07-03
- Volume
- 31
- Issue
- 9
- Pages
- 1964-1975
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11339506
- OpenAlex
- W4400307907
- Cited by
- 66
- References
- 21
- Citations by year
- 2026: 32 · 2025: 31 · 2024: 3
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (IIS-1741306, IIS-2235548)
- Department of Defense (DoD W91XWH-05-1-023)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, medical question answering, prompting, chain-of-thought prompting, self-consistency, response consistency, human-AI teaming
- MeSH terms
- Educational Measurement, Humans, Natural Language Processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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