GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations

Name: GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
Uploaded: 2026-09-02T21:12:09.844624+00:00
Duration: 345 s
Description: TriMediQ addresses scattered clinical facts in multi-turn medical conversations by converting patient responses into structured knowledge triplets. This creates a dynamic, patient-specific Knowledge Graph that allows for complex reasoning. The system shows significant gains in accuracy and scalability compared to traditional methods, providing a verifiable path forward for medical AI.

TriMediQ addresses scattered clinical facts in multi-turn medical conversations by converting patient responses into structured knowledge triplets. This creates a dynamic, patient-specific Knowledge Graph that allows for complex reasoning. The system shows significant gains in accuracy and scalability compared to traditional methods, providing a verifiable path forward for medical AI.

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