Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909281502101504 |
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| author | Yu, Haoran Yu, Chang Wang, Zihan Zou, Dongxian Qin, Hao |
| author_facet | Yu, Haoran Yu, Chang Wang, Zihan Zou, Dongxian Qin, Hao |
| contents | In recent years, the application of Large Language Models (LLMs) in healthcare has shown significant promise in improving the accessibility and dissemination of medical knowledge. This paper presents a detailed study of various LLMs trained on the MedQuAD medical question-answering dataset, with a focus on identifying the most effective model for providing accurate medical information. Among the models tested, the Sentence-t5 combined with Mistral 7B demonstrated superior performance, achieving a precision score of 0.762. This model's enhanced capabilities are attributed to its advanced pretraining techniques, robust architecture, and effective prompt construction methodologies. By leveraging these strengths, the Sentence-t5 + Mistral 7B model excels in understanding and generating precise medical answers. Our findings highlight the potential of integrating sophisticated LLMs in medical contexts to facilitate efficient and accurate medical knowledge retrieval, thus significantly enhancing patient education and support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering Yu, Haoran Yu, Chang Wang, Zihan Zou, Dongxian Qin, Hao Computation and Language Artificial Intelligence In recent years, the application of Large Language Models (LLMs) in healthcare has shown significant promise in improving the accessibility and dissemination of medical knowledge. This paper presents a detailed study of various LLMs trained on the MedQuAD medical question-answering dataset, with a focus on identifying the most effective model for providing accurate medical information. Among the models tested, the Sentence-t5 combined with Mistral 7B demonstrated superior performance, achieving a precision score of 0.762. This model's enhanced capabilities are attributed to its advanced pretraining techniques, robust architecture, and effective prompt construction methodologies. By leveraging these strengths, the Sentence-t5 + Mistral 7B model excels in understanding and generating precise medical answers. Our findings highlight the potential of integrating sophisticated LLMs in medical contexts to facilitate efficient and accurate medical knowledge retrieval, thus significantly enhancing patient education and support. |
| title | Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.04138 |