Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine
Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-01-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e59069 |
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| author | Kuo Zhang Xiangbin Meng Xiangyu Yan Jiaming Ji Jingqian Liu Hua Xu Heng Zhang Da Liu Jingjia Wang Xuliang Wang Jun Gao Yuan-geng-shuo Wang Chunli Shao Wenyao Wang Jiarong Li Ming-Qi Zheng Yaodong Yang Yi-Da Tang |
| author_facet | Kuo Zhang Xiangbin Meng Xiangyu Yan Jiaming Ji Jingqian Liu Hua Xu Heng Zhang Da Liu Jingjia Wang Xuliang Wang Jun Gao Yuan-geng-shuo Wang Chunli Shao Wenyao Wang Jiarong Li Ming-Qi Zheng Yaodong Yang Yi-Da Tang |
| author_sort | Kuo Zhang |
| collection | DOAJ |
| description |
Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care. |
| format | Article |
| id | doaj-art-d89e3e1e8cf84e459df4ebc0f820f8f0 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-d89e3e1e8cf84e459df4ebc0f820f8f02025-01-07T16:30:27ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-01-0127e5906910.2196/59069Revolutionizing Health Care: The Transformative Impact of Large Language Models in MedicineKuo Zhanghttps://orcid.org/0000-0001-8152-0750Xiangbin Menghttps://orcid.org/0000-0002-4252-7869Xiangyu Yanhttps://orcid.org/0000-0002-1496-0358Jiaming Jihttps://orcid.org/0000-0002-3769-2077Jingqian Liuhttps://orcid.org/0009-0008-8949-9691Hua Xuhttps://orcid.org/0009-0001-4243-7943Heng Zhanghttps://orcid.org/0009-0000-5148-0743Da Liuhttps://orcid.org/0000-0003-1649-7296Jingjia Wanghttps://orcid.org/0000-0003-1556-9315Xuliang Wanghttps://orcid.org/0000-0001-6520-6948Jun Gaohttps://orcid.org/0000-0002-8599-4969Yuan-geng-shuo Wanghttps://orcid.org/0009-0008-9709-9954Chunli Shaohttps://orcid.org/0000-0001-6366-1285Wenyao Wanghttps://orcid.org/0000-0002-1296-9324Jiarong Lihttps://orcid.org/0000-0003-0549-5551Ming-Qi Zhenghttps://orcid.org/0000-0002-4300-0209Yaodong Yanghttps://orcid.org/0000-0001-8132-5613Yi-Da Tanghttps://orcid.org/0000-0002-9712-803X Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.https://www.jmir.org/2025/1/e59069 |
| spellingShingle | Kuo Zhang Xiangbin Meng Xiangyu Yan Jiaming Ji Jingqian Liu Hua Xu Heng Zhang Da Liu Jingjia Wang Xuliang Wang Jun Gao Yuan-geng-shuo Wang Chunli Shao Wenyao Wang Jiarong Li Ming-Qi Zheng Yaodong Yang Yi-Da Tang Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine Journal of Medical Internet Research |
| title | Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine |
| title_full | Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine |
| title_fullStr | Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine |
| title_full_unstemmed | Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine |
| title_short | Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine |
| title_sort | revolutionizing health care the transformative impact of large language models in medicine |
| url | https://www.jmir.org/2025/1/e59069 |
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