A Four Diagnostic Methods framework for assisting doctors in traditional Chinese medicine

Large-Scale Language Models (LLMs) have initiated transformative changes in Traditional Chinese Medicine (TCM), yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high implementation costs due to reliance on medical expertise. To address this, we propose a systematic framework integrating multimodal data and LLM technologies. First, we analyze bottlenecks in traditional diagnosis (e.g., subjectivity) and modernization challenges. The framework leverages open-source foundation models (e.g., Baichuan2, LLaMA) pre-trained on "symptomsyndromemedication" associations, fine-tuned with clinical data to simulate diagnostic workflows. Key components include: (1) a Data Input Layer capturing tongue image features (via YOLOv5s6/U-Net), speech spectra, BERT-encoded inquiry texts, and pulse waveforms; (2) a Feature Fusion Layer constructing syndrome differentiation vectors through multimodal feature concatenation; and (3) a Prediction & Feedback Layer generating diagnostic probabilities with reinforcement learning based on clinical efficacy. Finally, we discuss critical issues, including risks of physician replacement, professional competence degradation, and liability attribution in diagnostic errors. This framework aims to enhance TCM diagnostic efficiency while ensuring clinical reliability.