Large language models in medicine | RDL Research Database

Abstract

Large language models (LLMs) can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare settings. ChatGPT is a generative artificial intelligence (AI) chatbot produced through sophisticated fine-tuning of an LLM, and other tools are emerging through similar developmental processes. Here we outline how LLM applications such as ChatGPT are developed, and we discuss how they are being leveraged in clinical settings. We consider the strengths and limitations of LLMs and their potential to improve the efficiency and effectiveness of clinical, educational and research work in medicine. LLM chatbots have already been deployed in a range of biomedical contexts, with impressive but mixed results. This review acts as a primer for interested clinicians, who will determine if and how LLM technology is used in healthcare for the benefit of patients and practitioners.

Keywords

MedicineComputer scienceIntensive care medicineComputational biologyBiology

MeSH Terms

HumansArtificial IntelligenceMedicineLanguageSoftwareTechnology

Affiliated Institutions

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Publication Info

Year: 2023
Type: review
Volume: 29
Issue: 8
Pages: 1930-1940
Citations: 2489
Access: Closed

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APA Style

                            
                                
                                    Arun James Thirunavukarasu, 
                                
                                    Darren Shu Jeng Ting, 
                                
                                    Kabilan Elangovan
                                
                                et al.
                            
                            (2023). 
                            Large language models in medicine. 
                            Nature Medicine
                            , 29
                            (8)
                            , 1930-1940.
                            https://doi.org/10.1038/s41591-023-02448-8
                        

Identifiers

DOI: 10.1038/s41591-023-02448-8
PMID: 37460753

Data Quality

Data completeness: 86%