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Clusmann, J.* ; Kolbinger, F.R.* ; Muti, H.S.* ; Carrero, Z.I.* ; Eckardt, J.N.* ; Laleh, N.G.* ; Löffler, C.M.L.* ; Schwarzkopf, S.C.* ; Unger, M.* ; Veldhuizen, G.P.* ; Wagner, S. ; Kather, J.N.*

The future landscape of large language models in medicine.

Commun. Med. 3:141 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and generate text. LLMs attracted substantial public attention after OpenAI's ChatGPT was made publicly available in November 2022. LLMs can often answer questions, summarize, paraphrase and translate text on a level that is nearly indistinguishable from human capabilities. The possibility to actively interact with models like ChatGPT makes LLMs attractive tools in various fields, including medicine. While these models have the potential to democratize medical knowledge and facilitate access to healthcare, they could equally distribute misinformation and exacerbate scientific misconduct due to a lack of accountability and transparency. In this article, we provide a systematic and comprehensive overview of the potentials and limitations of LLMs in clinical practice, medical research and medical education.
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Publication type Article: Journal article
Document type Review
Keywords Patient Safety; Communication; Impact; Care
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 2730-664X
e-ISSN 2730-664X
Journal Communications Medicine
Quellenangaben Volume: 3, Issue: 1, Pages: , Article Number: 141 Supplement: ,
Publisher Springer
Publishing Place Campus, 4 Crinan St, London, N1 9xw, England
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-530006-001
Grants German Academic Exchange Service (SECAI)
German Federal Ministry of Education and Research (PEARL)
Max-Eder-Programme of the German Cancer Aid
German Federal Ministry of Health (DEEP LIVER)
BMBF (Federal Ministry of Education and Research) in DAAD project as part of the program Konrad Zuse Schools of Excellence in Artificial Intelligence
Helmholtz Association under the joint research school "Munich School for Data Science-MUDS"
Add-on Fellowship of the Joachim Herz Foundation
DOI 10.1038/s43856-023-00370-1
WOS ID 001078603900001
Scopus ID 85204247190
PubMed ID 37816837
Erfassungsdatum 2023-11-28
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