arXiv cs.AI· Asad Gohar, Md Ariful Islam Mozumder, Moon-Il Joo, Hee-Cheol Kim·· 12 小时前AI 评分29
DocTalk：用 LoRA 微调与思维链推理提升临床决策支持的医疗知识检索
DocTalk: Enhancing Medical Knowledge Retrieval via Fine-tuned Large Language Models with Chain-of-Thought Reasoning for Robust Clinical Decision Support
AI 导读
DocTalk 是一套面向医生的问答系统，可从去标识化出院小结生成简洁的图表依据型回答，并已部署为安全的网页平台用于交互式 EHR 查询。
正文
Abstract:Objective: The rapid advancement of large language models (LLMs) in healthcare has opened new possibilities for AI-assisted clinical decision-making; however, safe and effective deployment remains challenging because of hallucination and domain misalignment. This study introduces DocTalk, a doctor-assistive question-answering (QA) system designed to generate concise, chart-grounded responses from de-identified discharge summaries. Methods: We developed a high-fidelity data generation pipeline that produces 500 clinically relevant QA pairs through sentence-level decomposition, chain-of-thought-guided question formulation, and expert review. Six open-source LLMs, including three general-purpose and three biomedical models, were evaluated before and after parameter-efficient instruction tuning using Low-Rank Adaptation (LoRA). The framework integrates prompt engineering, in-context learning, and medically aligned evaluation to support factual and interpretable responses. Results: On DocTalk-QA, instruction tuning produced consistent improvements across lexical, semantic, and clinical fidelity metrics. The best-performing tuned model, Llama-3-Med42-8B, achieved ROUGE-L = 0.40, BERTScore-F1 = 0.76, and UMLS-F1 = 0.49. To examine external generalization beyond the DocTalk-QA dataset, the two best-performing models, Llama-3.1-8B-Instruct and Llama-3-Med42-8B, were evaluated on PubMedQA before and after instruction tuning. Accuracy improved from 0.931 to 0.977 for Llama-3.1-8B-Instruct and from 0.935 to 0.943 for Llama-3-Med42-8B. Conclusion: DocTalk was deployed as a secure web-based platform for interactive EHR querying. These findings suggest that clinically grounded instruction tuning can improve medical QA performance and support practical clinical decision support.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.34871 [cs.AI] |
| (or arXiv:2610.34871v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34871 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Md Ariful Islam Mozumder [view email]
[v1]
Mon, 26 Oct 2026 05:35:26 UTC (1,016 KB)
来源：arXiv cs.AI · arxiv.org