LingLan: An Advancing Traditional Chinese Medicine Diagnosis LLM with Multimodal Data

Abstract:Though artificial intelligence (AI) increasingly transforms modern medicine, its integration into Traditional Chinese Medicine (TCM) has been relatively slow, primarily due to TCM's reliance on holistic, subjective diagnostic methods---namely Inspection, Auscultation and Olfaction, Inquiry, and Palpation(I-AOI-P)---which are difficult to align with quantitative, standardized medical systems. In this work, we introduce a Unification Framework for Multimodal Data (UFMD), which automatically processes tongue and pulse images into structured, clinically standard descriptions, integrating multi-source diagnostic information into a unified digital record of I-AOI-P process. Building on this structured data, we create LingLan-14B, a TCM-specific large language model fine-tuned via supervised learning to emulate the diagnostic logic and workflow of I-AOI-P process. Experimental results show that our method significantly enhances diagnostic accuracy, achieving a relative improvement of 103.5% over the baseline (62.72% vs. 30.82%) and reaching an F1-score of up to 82%.

Artificial Intelligence (cs.AI)

arXiv:2609.25715 [cs.AI]

(or arXiv:2609.25715v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.25715 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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