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

2026-09-22

AI Summary

本文通过构建多模态数据统一框架和特定于中医的大规模语言模型LingLan-14B，解决了中医诊断方法难以与现代医学系统结合的问题，显著提高了诊断准确性。

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%.

Problem

• Traditional Chinese Medicine
• artificial intelligence
• diagnostic methods
• multimodal data
• standardized medical systems

Innovation

• Unification Framework for Multimodal Data (UFMD)
• LingLan-14B
• TCM-specific large language model
• I-AOI-P process