Large language models in health care: Development, applications, and challenges
Abstract Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have be...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | Health Care Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/hcs2.61 |
| _version_ | 1797738425583927296 |
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| author | Rui Yang Ting Fang Tan Wei Lu Arun James Thirunavukarasu Daniel Shu Wei Ting Nan Liu |
| author_facet | Rui Yang Ting Fang Tan Wei Lu Arun James Thirunavukarasu Daniel Shu Wei Ting Nan Liu |
| author_sort | Rui Yang |
| collection | DOAJ |
| description | Abstract Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre‐training. In the last 3 years, these domain‐specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs. |
| first_indexed | 2024-03-12T13:43:36Z |
| format | Article |
| id | doaj.art-ea9e394cc4a1469d9c9e59eb9b54ef5f |
| institution | Directory Open Access Journal |
| issn | 2771-1757 |
| language | English |
| last_indexed | 2024-03-12T13:43:36Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Care Science |
| spelling | doaj.art-ea9e394cc4a1469d9c9e59eb9b54ef5f2023-08-23T11:16:21ZengWileyHealth Care Science2771-17572023-08-012425526310.1002/hcs2.61Large language models in health care: Development, applications, and challengesRui Yang0Ting Fang Tan1Wei Lu2Arun James Thirunavukarasu3Daniel Shu Wei Ting4Nan Liu5Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore SingaporeSingapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore SingaporeStatNLP Research Group Singapore University of Technology and Design SingaporeUniversity of Cambridge School of Clinical Medicine Cambridge UKSingapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore SingaporeDuke‐NUS Medical School Centre for Quantitative Medicine Singapore SingaporeAbstract Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre‐training. In the last 3 years, these domain‐specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.https://doi.org/10.1002/hcs2.61Large language modelAIHealth care |
| spellingShingle | Rui Yang Ting Fang Tan Wei Lu Arun James Thirunavukarasu Daniel Shu Wei Ting Nan Liu Large language models in health care: Development, applications, and challenges Health Care Science Large language model AI Health care |
| title | Large language models in health care: Development, applications, and challenges |
| title_full | Large language models in health care: Development, applications, and challenges |
| title_fullStr | Large language models in health care: Development, applications, and challenges |
| title_full_unstemmed | Large language models in health care: Development, applications, and challenges |
| title_short | Large language models in health care: Development, applications, and challenges |
| title_sort | large language models in health care development applications and challenges |
| topic | Large language model AI Health care |
| url | https://doi.org/10.1002/hcs2.61 |
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