Empowering medical systematic reviews with large language models: methods, development directions, and applications
With the exponential growth of biomedical literature, traditional keyword-based retrieval methods are increasingly inadequate for meeting the dual demands of efficiency and precision in clinical and research contexts. In recent years, large language models (LLMs), exemplified by ChatGPT and DeepSeek...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Guangxi Medical University
2025-06-01
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| Series: | Guangxi Yike Daxue xuebao |
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| Online Access: | https://journal.gxmu.edu.cn/article/doi/10.16190/j.cnki.45-1211/r.2025.03.001 |
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| _version_ | 1849426539993628672 |
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| author | Yannan HUANG Haoran SANG Yu LIU Liantao MA Yinghao ZHU |
| author_facet | Yannan HUANG Haoran SANG Yu LIU Liantao MA Yinghao ZHU |
| author_sort | Yannan HUANG |
| collection | DOAJ |
| description | With the exponential growth of biomedical literature, traditional keyword-based retrieval methods are increasingly inadequate for meeting the dual demands of efficiency and precision in clinical and research contexts. In recent years, large language models (LLMs), exemplified by ChatGPT and DeepSeek, have demonstrated significant potential in supporting medical systematic reviews due to their powerful natural language processing capabilities. However, its inherent challenges such as the"hallucination"problem and lagging knowledge update limit the reliability of its direct application. This paper systematically introduces six core technical approaches currently used to mitigate hallucinations in LLMs, with a particular focus on explaining the principles and application advantages of retrieval-augmented generation (RAG). After comprehensively reviewing the technical characteristics and application scenarios of 22 representative studies in the context of systematic reviews, the paper further identifies LLMs capable of"structured understanding and generation based on levels of evidence"as one of the key future directions. The goal is to provide systematic guidance for medical researchers and clinical practitioners, helping them make scientific and efficient use of LLMs to enhance the efficiency of biomedical literature processing and the quality of evidence-based medical decision-making. |
| format | Article |
| id | doaj-art-2ff8d4c1552946fa9d8d4a53b9dbf68f |
| institution | Kabale University |
| issn | 1005-930X |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Journal of Guangxi Medical University |
| record_format | Article |
| series | Guangxi Yike Daxue xuebao |
| spelling | doaj-art-2ff8d4c1552946fa9d8d4a53b9dbf68f2025-08-20T03:29:21ZzhoEditorial Office of Journal of Guangxi Medical UniversityGuangxi Yike Daxue xuebao1005-930X2025-06-0142332333110.16190/j.cnki.45-1211/r.2025.03.001gxykdxxb-42-3-323Empowering medical systematic reviews with large language models: methods, development directions, and applicationsYannan HUANG0Haoran SANG1Yu LIU2Liantao MA3Yinghao ZHU4Peking University, Beijing 100871, ChinaPeking University, Beijing 100871, ChinaUniversity of Oxford, Oxford OX2 0JB, United KingdomPeking University, Beijing 100871, ChinaPeking University, Beijing 100871, ChinaWith the exponential growth of biomedical literature, traditional keyword-based retrieval methods are increasingly inadequate for meeting the dual demands of efficiency and precision in clinical and research contexts. In recent years, large language models (LLMs), exemplified by ChatGPT and DeepSeek, have demonstrated significant potential in supporting medical systematic reviews due to their powerful natural language processing capabilities. However, its inherent challenges such as the"hallucination"problem and lagging knowledge update limit the reliability of its direct application. This paper systematically introduces six core technical approaches currently used to mitigate hallucinations in LLMs, with a particular focus on explaining the principles and application advantages of retrieval-augmented generation (RAG). After comprehensively reviewing the technical characteristics and application scenarios of 22 representative studies in the context of systematic reviews, the paper further identifies LLMs capable of"structured understanding and generation based on levels of evidence"as one of the key future directions. The goal is to provide systematic guidance for medical researchers and clinical practitioners, helping them make scientific and efficient use of LLMs to enhance the efficiency of biomedical literature processing and the quality of evidence-based medical decision-making.https://journal.gxmu.edu.cn/article/doi/10.16190/j.cnki.45-1211/r.2025.03.001large language modelsmedical systematic reviewsretrieval-augmented generationprompt engineering |
| spellingShingle | Yannan HUANG Haoran SANG Yu LIU Liantao MA Yinghao ZHU Empowering medical systematic reviews with large language models: methods, development directions, and applications Guangxi Yike Daxue xuebao large language models medical systematic reviews retrieval-augmented generation prompt engineering |
| title | Empowering medical systematic reviews with large language models: methods, development directions, and applications |
| title_full | Empowering medical systematic reviews with large language models: methods, development directions, and applications |
| title_fullStr | Empowering medical systematic reviews with large language models: methods, development directions, and applications |
| title_full_unstemmed | Empowering medical systematic reviews with large language models: methods, development directions, and applications |
| title_short | Empowering medical systematic reviews with large language models: methods, development directions, and applications |
| title_sort | empowering medical systematic reviews with large language models methods development directions and applications |
| topic | large language models medical systematic reviews retrieval-augmented generation prompt engineering |
| url | https://journal.gxmu.edu.cn/article/doi/10.16190/j.cnki.45-1211/r.2025.03.001 |
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