ArticleJournal of the American Medical Informatics Association : JAMIA2024
Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Leveraging Generative Large Language Models for Temporal Relation Extraction From French Clinical Narratives: Prompt-Chaining Approach.Journal of medical Internet research · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models.Scientific reports · 2026Article
- Review
- XuanHuGPT: parameter-efficient fine-tuning of large language model in the field of traditional Chinese medicine.Chinese medicine · 2025Article
- Current Landscape and Future Directions Regarding Generative Large Language Models in Stroke Care: Scoping Review.JMIR medical informatics · 2025Article
- Iterative refinement and goal articulation to optimize large language models for clinical information extraction.NPJ digital medicine · 2025Article
- Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models.medRxiv : the preprint server for health sciences · 2025Article
- Decoding substance use disorder severity from clinical notes using a large language model.Npj mental health research · 2025Article
- An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.Journal of medical Internet research · 2025Article
- Identifying Opioid Overdose and Opioid Use Disorder and Related Information from Clinical Narratives Using Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Prompt Engineering Paradigms for Medical Applications: Scoping Review.Journal of medical Internet research · 2024Article
- LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.
methodsWe formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. We adopted soft prompts (ie, trainable vectors) with frozen LLM, where the LLM parameters were not updated (ie, frozen) and only the vectors of soft prompts were updated, known as prompt tuning. We added additional soft prompts as a prefix to the input layer, which were optimized during the prompt tuning. We evaluated the proposed method using 7 clinical NLP tasks and compared them with previous task-specific solutions based on Transformer models. RESULTS AND
conclusionThe proposed approach achieved state-of-the-art performance for 5 out of 7 major clinical NLP tasks using one unified generative LLM. Our approach outperformed previous task-specific transformer models by ∼3% for concept extraction and 7% for relation extraction applied to social determinants of health, 3.4% for clinical concept normalization, 3.4%-10% for clinical abbreviation disambiguation, and 5.5%-9% for natural language inference. Our approach also outperformed a previously developed prompt-based machine reading comprehension (MRC) model, GatorTron-MRC, for clinical concept and relation extraction. The proposed approach can deliver the "one model for all" promise from training to deployment using a unified generative LLM.
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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.