Large language models in medicine
- PMID: 37460753
- DOI: 10.1038/s41591-023-02448-8
Large language models in medicine
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.
© 2023. Springer Nature America, Inc.
References
-
- Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019). - PubMed
-
- Liddy, E. Natural language processing. In Encyclopedia of Library and Information Science (eds Kent, A. & Lancour, H.)(Marcel Decker, 2001).
-
- Khurana, D., Koli, A., Khatter, K. & Singh, S. Natural language processing: state of the art, current trends and challenges. Multimed. Tools Appl. 82, 3713–3744 (2023). - PubMed
-
- Brown, T. et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems Vol. 33 1877–1901 (Curran Associates, 2020).
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