Using fine-tuned large language models to parse clinical notes in musculoskeletal pain disorders
Open accessAbstract
Musculoskeletal disorders like lower back, knee, and shoulder pain create a substantial health burden in developed countries—affecting function, mobility, and quality of life1. These conditions are often multifactorial and require clinicians to thoroughly assess their cause to decide on appropriate investigative and treatment approaches. However, the electronic health-care record does not explicitly capture pain characteristics within ICD-10 billing codes, making time-consuming and error-prone retrospective chart review necessary.
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LLMs have shown the ability to encode clinical knowledge.
“Recently, LLMs have shown the ability to encode clinical knowledge7,8, retrieve relevant medical texts9, and perform accurate single-turn medical question-answering3,10–12.”
From AgentClinic: a multimodal benchmark for tool-using clinical AI agents (2026) · cites this paper as [8]
The Results passage under “Using fine-tuned large language models to parse clinical notes in musculoskeletal pain disorders” (page unavailable) reports that fine-tuned LLMs classified pain location and acuity from clinical notes. This demonstrates an ability to learn and use specific clinical information, but the paper does not establish the broader claim that LLMs encode clinical knowledge generally. (LLM, conf=0.78)
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Training language models to follow instructions with human feedback
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LoRA: Low-Rank Adaptation of Large Language Models
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AgentClinic: a multimodal benchmark for tool-using clinical AI agents
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“Recently, LLMs have shown the ability to encode clinical knowledge 7,8 , retrieve relevant medical texts 9 , and perform accurate single-turn medical question-answering 3,10 – 12 .”
- Authors
- Vaid, A., Landi, I., Nadkarni, G., & Nabeel, I.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Vaid, Akhil
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 5
- Issue
- 12
- Pages
- e855-e858
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W4387970096
- Cited by
- 45
- References
- 10
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 15
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institute for Occupational Safety and Health
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, fine-tuning, electronic health records, low back pain, knee pain, shoulder pain, retrospective chart review, natural language processing
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