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A Study of Generative Large Language Model for Medical Research and Healthcare

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arxiv 2305.13523 v1 pith:SUK4XLW6 submitted 2023-05-22 cs.CL

Cheng Peng , Xi Yang , Aokun Chen , Kaleb E Smith , Nima PourNejatian , Anthony B Costa , Cheryl Martin , Mona G Flores
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Ying Zhang Tanja Magoc Gloria Lipori Duane A Mitchell Naykky S Ospina Mustafa M Ahmed William R Hogan Elizabeth A Shenkman Yi Guo Jiang Bian Yonghui Wu
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classification cs.CL
keywords gatortrongptclinicalhealthcarelanguagellmsmedicalmodelsresearch
verification ladder T0 review T1 audit T2 compute T3 formal
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There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical natural language processing for medical research. Synthetic NLP models trained using GatorTronGPT generated text outperform NLP models trained using real-world clinical text. Physicians Turing test using 1 (worst) to 9 (best) scale shows that there is no significant difference 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 on the opportunities and challenges of LLMs for medical research and healthcare.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs

    cs.CL 2025-07 conditional novelty 6.0 of 10

    The paper introduces the first unified benchmark for LLM-generated patent abstracts and reports that GPT-4o and Llama 3 produce abstracts with high BERTScore and useful downstream task performance.

  2. MIRA: A Novel Framework for Fusing Modalities in Medical RAG

    cs.CV 2025-07 reject novelty 4.0 of 10

    A medical multimodal RAG pipeline with rethink-and-rearrange and online search; the claimed SOTA is contradicted by the paper's own PMC-VQA numbers.

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