ArticleNPJ digital medicine2025
Small language models learn enhanced reasoning skills from medical textbooks.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Small language models in medicine.Nature biomedical engineering · 2026Article
- A Locally Executable AI System for Improving Preoperative Patient Communication: Multidomain Clinical Evaluation.JMIR medical informatics · 2026Article
- Real-world evaluation of large language model for patients medical and administrative queries in nuclear medicine.NPJ digital medicine · 2026Article
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Gender-Attributed Persona Prompts and the Diagnostic Accuracy of Proprietary and Open-Weight Large Language Models in Chagas Disease and Visceral Leishmaniasis: A Paired Experimental Study.Healthcare (Basel, Switzerland) · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- Artificial intelligence-assisted quality assessment of mid-trimester ultrasound examinations using large vision-language models.BMC pregnancy and childbirth · 2026Article
- Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?JCO clinical cancer informatics · 2026Article
- Review
- Domain-adapted language model using reinforcement learning for various dementias.medRxiv : the preprint server for health sciences · 2026Article
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
- Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.Diagnostics (Basel, Switzerland) · 2026Review
- Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.Sleep advances : a journal of the Sleep Research Society · 2026Article
- From Zero-Shot to Bedside: A Practical Playbook for Adapting Open-Source Large Language Models to Clinical Symptom Extraction.Proceedings of machine learning research · 2026Article
- Token-Level Attribution for Transparent Biomedical AI.Biomedical engineering and computational biology · 2026Article
- Context matching is not reasoning when performing generalized clinical evaluation of generative language models.NPJ digital medicine · 2025Article
- Automating expert-level medical reasoning evaluation of large language models.NPJ digital medicine · 2025Article
- RAG-Enhanced Open SLMs for Hypertension Management Chatbots.Journal of medical systems · 2025Article
- Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
Abstract
Small language models (SLM) offer promise for medical applications by addressing the privacy and hardware constraints of large language models; however, their limited parameters (often fewer than ten billion) hinder multi-step reasoning for complex medical tasks. This study presents Meerkat, a new family of medical SLMs designed to be lightweight while enhancing reasoning capabilities. We begin by designing an effective and efficient training method. This involves extracting high-quality chain-of-thought reasoning paths from 18 medical textbooks, which are then combined with diverse instruction-following datasets within the medical domain, totaling 441K training examples. Fine-tuning was conducted on open-source SLMs using this curated dataset. Our Meerkat-7B and Meerkat-8B models outperformed their counterparts by 22.3% and 10.6% across six exam datasets, respectively. They also improved scores on the NEJM Case Challenge from 7 to 16 and from 13 to 20, surpassing the human score of 13.7. Additionally, they demonstrated superiority in expert evaluations, excelling in all metrics-completeness, factuality, clarity, and logical consistency-of reasoning abilities.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.