Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine
Large language models have advanced medical reasoning, but clinically grounded reasoning in traditional Chinese medicine (TCM) remains challenging because prescription decisions require integrating domain knowledge, syndrome differentiation, treatment principles, herbal prescription composition,…
This article presents Lingdan-V2, a family of large language models designed for clinically grounded reasoning in traditional Chinese medicine (TCM), addressing challenges in integrating domain-specific knowledge for prescription decisions.
What happened
Researchers developed Lingdan-V2, a TCM-specific language model family, using a clinical alignment framework that integrates domain knowledge, syndrome differentiation, and prescription optimization through reinforcement learning. The models were evaluated on public and private datasets, demonstrating improved performance in herbal prescription generation compared to existing systems.
Why it matters
The work addresses a critical gap in applying AI to TCM, where clinical decision-making requires complex integration of domain-specific knowledge. Improved prescription accuracy could enhance clinical decision support systems in traditional medicine.
- Domain-specific pretraining improves clinical reasoning in AI models
- Reinforcement learning with clinical feedback enhances prescription accuracy
- Model size alone does not guarantee better performance in specialized tasks
- Expert variability poses challenges for AI training in medical domains
The bigger picture
The models provide a scalable approach for domain-specific AI in healthcare, demonstrating that specialized training can outperform larger general-purpose models in niche medical applications. However, variability in physician expertise affects reward stability in RL training.
Lingdan-V2 demonstrates the potential of AI to address complex clinical reasoning in traditional Chinese medicine through specialized architecture and reinforcement learning, offering a framework for domain-grounded medical AI development.
Technical details
- Staged clinical alignment framework: pretraining on TCM data, complementary reasoning supervision, prescription-centered RL
- Structured reinforcement learning with prescription-level feedback
- F1 score metrics for evaluating herb-level and prescription-level accuracy
- Parameter scales: 4B, 8B, 14B
FAQ
What makes Lingdan-V2 different from general-purpose language models?
Lingdan-V2 is specifically trained on TCM data with a clinical alignment framework that integrates syndrome differentiation, treatment principles, and prescription optimization through reinforcement learning.
How does the model handle dosage determination?
The model's reinforcement learning component improves dosage agreement in clinical datasets, though the article does not specify the exact mechanism for dosage calculation.
What benchmarks were used to evaluate the models?
The models were tested on a public benchmark with 25,620 TCM items across 13 tasks and a private dataset of 4,348 spleen and stomach disease prescriptions.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.CL (NLP) on Oct 14, 2026.
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