Applications and Future Prospects of Medical LLMs: A Survey Based on the M-KAT Conceptual Framework.
The success of large language models (LLMs) in general areas have sparked a wave of research into their applications in the medical field. However, enhancing the medical professionalism of these models remains a major challenge. This study proposed a novel model training theoretical framework, the M...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 19 |
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| Autores principales: | , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
12/27/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182881811&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182881811 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 12/27/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182881811 182881811 182881811 10.1007/s10916-024-02132-5 182881811 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Applications and Future Prospects of Medical LLMs: A Survey Based on the M-KAT Conceptual Framework. aug: au: Chang, Ying Yin, Jian-ming Li, Jian-min Liu, Chang Cao, Ling-yong Lin, Shu-yuan affil: https://ror.org/04epb4p87 School of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, 310053, Hangzhou, China sug: subj: Deep Learning Trends Natural Language Processing Models, Theoretical Professionalism Skill Acquisition Critical Thinking Surveys Conceptual Framework ab: The success of large language models (LLMs) in general areas have sparked a wave of research into their applications in the medical field. However, enhancing the medical professionalism of these models remains a major challenge. This study proposed a novel model training theoretical framework, the M-KAT framework, which integrated domain-specific training methods for LLMs with the unique characteristics of the medical discipline. This framework aimed to improve the medical professionalism of the models from three perspectives: general knowledge acquisition, specialized skill development, and alignment with clinical thinking. This study summarized the outcomes of medical LLMs across four tasks: clinical diagnosis and treatment, medical question answering, medical research, and health management. Using the M-KAT framework, we analyzed the contribution to enhancement of professionalism of models through different training stages. At the same time, for some of the potential risks associated with medical LLMs, targeted solutions can be achieved through pre-training, SFT, and model alignment based on cultivated professional capabilities. Additionally, this study identified main directions for future research on medical LLMs: advancing professional evaluation datasets and metrics tailored to the needs of medical tasks, conducting in-depth studies on medical multimodal large language models (MLLMs) capable of integrating diverse data types, and exploring the forms of medical agents and multi-agent frameworks that can interact with real healthcare environments and support clinical decision-making. It is hoped that predictions of work can provide a reference for subsequent research. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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