Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866917594831781888 |
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| author | Ren, Zhiyao Zhan, Yibing Yu, Baosheng Ding, Liang Tao, Dacheng |
| author_facet | Ren, Zhiyao Zhan, Yibing Yu, Baosheng Ding, Liang Tao, Dacheng |
| contents | The copilot framework, which aims to enhance and tailor large language models (LLMs) for specific complex tasks without requiring fine-tuning, is gaining increasing attention from the community. In this paper, we introduce the construction of a Healthcare Copilot designed for medical consultation. The proposed Healthcare Copilot comprises three main components: 1) the Dialogue component, responsible for effective and safe patient interactions; 2) the Memory component, storing both current conversation data and historical patient information; and 3) the Processing component, summarizing the entire dialogue and generating reports. To evaluate the proposed Healthcare Copilot, we implement an auto-evaluation scheme using ChatGPT for two roles: as a virtual patient engaging in dialogue with the copilot, and as an evaluator to assess the quality of the dialogue. Extensive results demonstrate that the proposed Healthcare Copilot significantly enhances the capabilities of general LLMs for medical consultations in terms of inquiry capability, conversational fluency, response accuracy, and safety. Furthermore, we conduct ablation studies to highlight the contribution of each individual module in the Healthcare Copilot. Code will be made publicly available on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13408 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation Ren, Zhiyao Zhan, Yibing Yu, Baosheng Ding, Liang Tao, Dacheng Computation and Language The copilot framework, which aims to enhance and tailor large language models (LLMs) for specific complex tasks without requiring fine-tuning, is gaining increasing attention from the community. In this paper, we introduce the construction of a Healthcare Copilot designed for medical consultation. The proposed Healthcare Copilot comprises three main components: 1) the Dialogue component, responsible for effective and safe patient interactions; 2) the Memory component, storing both current conversation data and historical patient information; and 3) the Processing component, summarizing the entire dialogue and generating reports. To evaluate the proposed Healthcare Copilot, we implement an auto-evaluation scheme using ChatGPT for two roles: as a virtual patient engaging in dialogue with the copilot, and as an evaluator to assess the quality of the dialogue. Extensive results demonstrate that the proposed Healthcare Copilot significantly enhances the capabilities of general LLMs for medical consultations in terms of inquiry capability, conversational fluency, response accuracy, and safety. Furthermore, we conduct ablation studies to highlight the contribution of each individual module in the Healthcare Copilot. Code will be made publicly available on GitHub. |
| title | Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.13408 |