DeepSeek-Med-8B: Medical LLM for Chinese Diagnosis and Referral

The uneven distribution of medical resources in China poses significant challenges, especially in rural areas. While large language models (LLMs) offer potential for clinical support, existing systems like GPT-4 and Med-PaLM suffer from hallucinations, English-centric biases, and lack real-time physician integration. We present DeepSeek-Med-8B, a Chinese medical conversational agent based on the DeepSeek-R1-DistillLlama-8B architecture.DeepSeek-Med-8B is trained through: (i) Supervised Fine-Tuning (SFT) on curated Chinese medical corpora; (ii) Reinforcement Learning with AI and Doctor Feedback (RLAIF) for factuality, empathy, and referral quality; and (iii) Retrieval-Augmented Generation (RAG) for real-time grounding in physician databases.Across eight clinical tasks, DeepSeek-Med-8B achieves a top1 mean score of 66.9 on GPT-4o-based benchmarks and a 74% top-3 doctor match rate, outperforming rule-based baselines. The model runs efficiently on a single RTX 4090 GPU via INT8 quantization.

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Published
2025-07-11
DOI
https://doi.org/10.1109/cisat66811.2025.11181817
Primary Topic
Biomedical Text Mining and Ontologies
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article
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DeepSeek-Med-8B: Medical LLM for Chinese Diagnosis and Referral

Bin Liu, Chenxing Li, Jiawei Mao, Wei Luo
Biomedical Text Mining and Ontologies
article

DeepSeek-Med-8B: Medical LLM for Chinese Diagnosis and Referral

Bin Liu, Chenxing Li, Jiawei Mao, Wei Luo
article en

Abstract

The uneven distribution of medical resources in China poses significant challenges, especially in rural areas. While large language models (LLMs) offer potential for clinical support, existing systems like GPT-4 and Med-PaLM suffer from hallucinations, English-centric biases, and lack real-time physician integration. We present DeepSeek-Med-8B, a Chinese medical conversational agent based on the DeepSeek-R1-DistillLlama-8B architecture.DeepSeek-Med-8B is trained through: (i) Supervised Fine-Tuning (SFT) on curated Chinese medical corpora; (ii) Reinforcement Learning with AI and Doctor Feedback (RLAIF) for factuality, empathy, and referral quality; and (iii) Retrieval-Augmented Generation (RAG) for real-time grounding in physician databases.Across eight clinical tasks, DeepSeek-Med-8B achieves a top1 mean score of 66.9 on GPT-4o-based benchmarks and a 74% top-3 doctor match rate, outperforming rule-based baselines. The model runs efficiently on a single RTX 4090 GPU via INT8 quantization.

Panzhihua University (CN)
Openalex Percentile: Top 76%
Biomedical Text Mining and Ontologies
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DeepSeek-Med-8B: Medical LLM for Chinese Diagnosis and Referral — Bin Liu, Chenxing Li, et al. · (2025) | TGRS Research Map | TGRS