ArticleNPJ digital medicine2022
A large language model for electronic health records.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 390 papers, 4 of them syntheses that pooled it.
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.
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Who cites it
390 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Testing and evaluation of generative large language models in electronic health record applications: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review.International journal of medical informatics · 2026Pooled it
- Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review.JMIR aging · 2025Pooled it
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- Misaligned by design: evaluating and deploying generative AI for the real-world conditions of primary care: a Nordic and European perspective.Scandinavian journal of primary health care · 2026Article
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- MedNLI-Pain benchmarks a minimally necessary requirement for large language model alignment.JAMIA open · 2026Article
- Article
- Computable longitudinal patient journeys from structured and unstructured EHR data.Nature medicine · 2026Article
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- Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models.Scientific reports · 2026Article
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- AI agents in cancer imaging: Concepts, advances, and clinical perspectives.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- PORT: Generative EHR Pretraining for Pediatric Intraoperative Risk Prediction.Research square · 2026Article
- Dataset of anonymized clinical events and notes from obstetric electronic health records for NLP.Data in brief · 2026Article
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- A vision-language pretrained transformer for versatile clinical respiratory disease applications.Nature biomedical engineering · 2026Article
- Inherited risk of coronary artery disease: redefining care with imaging and genetics.Nature reviews. Cardiology · 2026Review
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
330 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model-GatorTron-using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .
Identifiers
What 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.