ArticleAsian journal of andrology2026
ZhongdaChat-ED: a medical large language model for personalized erectile dysfunction health consultation and professional clinical decision-making using retrieval-augmented generation.
Article in Asian journal of andrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Identification of novel variants in SSX1, CPNE1, and SPTBN5 in men with oligoasthenoteratozoospermia using whole-genome sequencing.Journal of applied genetics · 2026Article
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9 authors.
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Abstract
Artificial intelligence (AI)-driven large language models (LLMs) hold potential for medical applications but face challenges, such as inaccurate or outdated training data. In this study, ZhongdaChat-ED, a personalized medical LLM integrating retrieval-augmented generation (RAG) technology, was developed to enhance erectile dysfunction (ED) counseling and clinical decision-making. The model was built using the open-source Deepseek-r1:32b framework, augmented with two specialized databases: a patient health consultation database and a clinical decision support database updated with real-time medical advancements. Two versions of ZhongdaChat-ED were developed: a Consumer Version for patient-facing health consultations and a Professional Version for clinician support. Performance was evaluated against four commonly used LLMs (ChatGPT4, Copilot, Claude, and Gemini) through simulated clinical consultations and case analyses. Three urologists and three patients assessed responses across various dimensions, including accuracy, human caring, ease of understanding, clinical significance, and informational frontier. The Consumer Version outperformed commonly used LLMs in accuracy (4.77/5), human caring (4.86/5), and ease of understanding (4.88/5) with all P < 0.001. The Professional Version demonstrated significantly higher clinical significance (>85.2% case score rate) and informational frontier scores (4.52/5) than those of other models ( P < 0.001). ZhongdaChat-ED effectively addresses limitations of conventional LLMs by leveraging RAG to integrate real-time, domain-specific data. ZhongdaChat-ED shows promise in enhancing patient health consultation and clinician decision-making for ED, underscoring the value of tailored AI systems in bridging gaps between generalized AI and specialized medical needs. Future work should expand multimodal capabilities and cross-disciplinary integration to broaden clinical utility.
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