ArticleNPJ digital medicine2023
A study of generative large language model for medical research and healthcare.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
160 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- The TRIPOD-LLM reporting guideline for studies using large language models: a Korean translation.Ewha medical journal · 2025Guideline
- Utilization of a HIPAA-compliant large language model chatbot in an academic pediatric medical center.PLOS digital health · 2026Article
- Knowledge Graph and Large Language Model-Based Analysis of fMRI Brain Functional Neuroimaging Research.Bioengineering (Basel, Switzerland) · 2026Article
- Mapping the generative era: a bibliometric and specialty-focused analysis of large language models in healthcare.Visual computing for industry, biomedicine, and art · 2026Article
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- Generating Question Prompt Lists From Electronic Health Record Data Using Large Language Models: Iterative Evaluation Study.Journal of medical Internet research · 2026Article
- Authoritative Textbook-Augmented Large Language Models for High-Altitude Public Health Medical Education in the Xizang Autonomous Region: Cross-Sectional Comparative Evaluation Study.Journal of medical Internet research · 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
- A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning.Journal of biomedical informatics · 2026Article
- Combining generative large language models and pretrained language models for predicting acute respiratory distress syndrome progression.Medical & biological engineering & computing · 2026Article
- Consistency in causal reasoning for large language models in scenarios of HIV antiretroviral treatment, drug interactions, and side effects.NPJ digital medicine · 2026Article
- Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- The Aloe Family recipe for open and specialized healthcare LLMs.NPJ digital medicine · 2026Article
- Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.Methods and protocols · 2026Article
- Biomedical multimodal large language models: From model-centric development to clinically grounded evaluation and integration.Journal of biomedical informatics · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.medRxiv : the preprint server for health sciences · 2026Article
- ASQ-PHI: An adversarial synthetic data benchmark for clinical de-identification and search utility.Data in brief · 2026Article
100 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 are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.
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.