Leveraging foundation and large language models in medical artificial intelligence
Abstract. Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as dis...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer
2024-11-01
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| Series: | Chinese Medical Journal |
| Online Access: | http://journals.lww.com/10.1097/CM9.0000000000003302 |
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| _version_ | 1850181161564766208 |
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| author | Io Nam Wong Olivia Monteiro Daniel T. Baptista-Hon Kai Wang Wenyang Lu Zhuo Sun Sheng Nie Yun Yin Jing Ni |
| author_facet | Io Nam Wong Olivia Monteiro Daniel T. Baptista-Hon Kai Wang Wenyang Lu Zhuo Sun Sheng Nie Yun Yin Jing Ni |
| author_sort | Io Nam Wong |
| collection | DOAJ |
| description | Abstract. Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as disease-specific, general-domain, and multi-modal models. The paper also addresses key challenges such as data acquisition and augmentation, including issues related to data volume, annotation, multi-modal fusion, and privacy concerns. Additionally, it discusses the evaluation, validation, limitations, and regulation of medical AI models, emphasizing their transformative potential in healthcare. The importance of continuous improvement, data security, standardized evaluations, and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications. |
| format | Article |
| id | doaj-art-ceaf37deda7c45dc950884942d9f93a9 |
| institution | OA Journals |
| issn | 0366-6999 2542-5641 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wolters Kluwer |
| record_format | Article |
| series | Chinese Medical Journal |
| spelling | doaj-art-ceaf37deda7c45dc950884942d9f93a92025-08-20T02:17:57ZengWolters KluwerChinese Medical Journal0366-69992542-56412024-11-01137212529253910.1097/CM9.0000000000003302202411050-00003Leveraging foundation and large language models in medical artificial intelligenceIo Nam Wong0Olivia Monteiro1Daniel T. Baptista-Hon2Kai Wang3Wenyang Lu4Zhuo Sun5Sheng Nie6Yun Yin7Jing Ni1 Institute for AI in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China1 Institute for AI in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China1 Institute for AI in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China2 Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100871, China4 Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China3 Department of Ophthalmology, The Third People’s Hospital of Changzhou, Changzhou, Jiangsu 203001, China5 Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China6 Faculty of Health and Wellness, Faculty of Business, City University of Macau, Macau Special Administrative Region 999078, ChinaAbstract. Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as disease-specific, general-domain, and multi-modal models. The paper also addresses key challenges such as data acquisition and augmentation, including issues related to data volume, annotation, multi-modal fusion, and privacy concerns. Additionally, it discusses the evaluation, validation, limitations, and regulation of medical AI models, emphasizing their transformative potential in healthcare. The importance of continuous improvement, data security, standardized evaluations, and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.http://journals.lww.com/10.1097/CM9.0000000000003302 |
| spellingShingle | Io Nam Wong Olivia Monteiro Daniel T. Baptista-Hon Kai Wang Wenyang Lu Zhuo Sun Sheng Nie Yun Yin Jing Ni Leveraging foundation and large language models in medical artificial intelligence Chinese Medical Journal |
| title | Leveraging foundation and large language models in medical artificial intelligence |
| title_full | Leveraging foundation and large language models in medical artificial intelligence |
| title_fullStr | Leveraging foundation and large language models in medical artificial intelligence |
| title_full_unstemmed | Leveraging foundation and large language models in medical artificial intelligence |
| title_short | Leveraging foundation and large language models in medical artificial intelligence |
| title_sort | leveraging foundation and large language models in medical artificial intelligence |
| url | http://journals.lww.com/10.1097/CM9.0000000000003302 |
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