Ó³»­´«Ã½

OBJECTIVE: Large language models (LLMs) offer promising potential in answering real-time medical queries, but they often produce lengthy, generic, and even hallucinatory responses. We aim to develop a reliable and interpretable medical dialogue system that incorporates clinical reasoning and then mitigates the risk of hallucination.METHODS: Two large datasets of real-world online consultation, MedDG and KaMed, were used for evaluation. We proposed a Medical Dialogue System with Knowledge Enhancement and Clinical Pathway Encoding (MedKP), which integrates an external medical knowledge graph and encodes internal clinical pathways to model physician reasoning. Performance was compared with state-of-the-art baselines, including GPT-4o and LLaMA3.1-70B. A multi-dimensional evaluation framework assessed (1) clinical relevance (medical entity-based), (2) textual similarity (ROUGE, BLEU), (3) semantic alignment (BERTScore), and (4) hallucination and consistency via an external LLM-based judge, as well as parallel human evaluation.RESULTS: Across both datasets, MedKP (6B) achieved the best overall performance, outperforming other advanced baselines and producing responses that align more closely with those of human physicians. For clinical relevance, MedKP reached a macro F1-score of medical entity at 31.41 on MedDG (previous best DFMed: 24.76, improved 30.41%) and 26.62 on KaMed (previous best LLaM-A3.1-70B: 20.67, improved 25.62%). Consistent improvements were observed across other metrics. Ablation studies further validated the effectiveness of each model component.CONCLUSION: Our results highlight the critical role of clinical reasoning in advancing trustworthy AI for digital healthcare. By enhancing the reliability, coherence, and transparency of AI-generated responses, this pathway-aware approach bridges the gap between LLMs and real-world clinical workflows, improving the accessibility of high-quality telemedicine services, particularly benefiting underserved populations.