ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024
Large Language Models for Efficient Medical Information Extraction.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- Automated RECIST tumor response classification through prompt-guided large language models.Scientific reports · 2026Article
- AI-assisted tumor board decision-making in pancreatic oncology.BMC medical informatics and decision making · 2026Article
- Auditable large language model curation of clinical notes refines glucagon-like peptide-1 initiation, persistence ascertainment, and compounding use beyond prescription records.Biology methods & protocols · 2026Article
- Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance.NPJ digital medicine · 2025Article
- Extracting epilepsy-related information from unstructured clinic letters using large language models.Epilepsia · 2025Article
- Clinical insights: A comprehensive review of language models in medicine.PLOS digital health · 2025Review
- Leveraging GPT-4o for Automated Extraction of Neural Projections from Scientific Literature.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Developing Large Language Model-based Pipeline for Identification of Disease Diagnosis: A Case Study on Identifying Newly Diagnosed Multiple Myeloma and its Precursor Disease in Veterans Health Administration Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extracting valuable insights from unstructured clinical narrative reports is a challenging yet crucial task in the healthcare domain as it allows healthcare workers to treat patients more efficiently and improves the overall standard of care. We employ ChatGPT, a Large language model (LLM), and compare its performance to manual reviewers. The review focuses on four key conditions: family history of heart disease, depression, heavy smoking, and cancer. The evaluation of a diverse sample of History and Physical (H&P) Notes, demonstrates ChatGPT's remarkable capabilities. Notably, it exhibits exemplary results in sensitivity for depression and heavy smokers and specificity for cancer. We identify areas for improvement as well, particularly in capturing nuanced semantic information related to family history of heart disease and cancer. With further investigation, ChatGPT holds substantial potential for advancements in medical information extraction.
Identifiers
38827084PMC11141860What OpenQuestion holds
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.