Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908589233274880 |
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| author | Xie, Wenya Xiao, Qingying Zheng, Yu Wang, Xidong Chen, Junying Ji, Ke Gao, Anningzhe Tiwari, Prayag Wan, Xiang Jiang, Feng Wang, Benyou |
| author_facet | Xie, Wenya Xiao, Qingying Zheng, Yu Wang, Xidong Chen, Junying Ji, Ke Gao, Anningzhe Tiwari, Prayag Wan, Xiang Jiang, Feng Wang, Benyou |
| contents | The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain expertise. To mitigate this, we propose repositioning LLMs as clinical assistants that collaborate with experienced physicians rather than interacting with patients directly. We conduct a two-stage inspiration-feedback survey to identify real-world needs in clinical workflows. Guided by this, we construct DoctorFLAN, a large-scale Chinese medical dataset comprising 92,000 Q&A instances across 22 clinical tasks and 27 specialties. To evaluate model performance in doctor-facing applications, we introduce DoctorFLAN-test (550 single-turn Q&A items) and DotaBench (74 multi-turn conversations). Experimental results with over ten popular LLMs demonstrate that DoctorFLAN notably improves the performance of open-source LLMs in medical contexts, facilitating their alignment with physician workflows and complementing existing patient-oriented models. This work contributes a valuable resource and framework for advancing doctor-centered medical LLM development |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11040 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks Xie, Wenya Xiao, Qingying Zheng, Yu Wang, Xidong Chen, Junying Ji, Ke Gao, Anningzhe Tiwari, Prayag Wan, Xiang Jiang, Feng Wang, Benyou Computation and Language The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain expertise. To mitigate this, we propose repositioning LLMs as clinical assistants that collaborate with experienced physicians rather than interacting with patients directly. We conduct a two-stage inspiration-feedback survey to identify real-world needs in clinical workflows. Guided by this, we construct DoctorFLAN, a large-scale Chinese medical dataset comprising 92,000 Q&A instances across 22 clinical tasks and 27 specialties. To evaluate model performance in doctor-facing applications, we introduce DoctorFLAN-test (550 single-turn Q&A items) and DotaBench (74 multi-turn conversations). Experimental results with over ten popular LLMs demonstrate that DoctorFLAN notably improves the performance of open-source LLMs in medical contexts, facilitating their alignment with physician workflows and complementing existing patient-oriented models. This work contributes a valuable resource and framework for advancing doctor-centered medical LLM development |
| title | Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.11040 |