Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
arXiv:2606.06869v1 Announce Type: new Abstract: Aim: Existing AI-assisted traditional Chinese medicine diagnostic tools suffer from opaque reasoning processes, passive interaction, and limited treatment plan presentation. This study proposes a knowledge-enhanced visual diagnostic system to improve the transparency and interpretability of syndrome differentiation and treatment. Methods: The system is built upon a Neo4j knowledge graph comprising 241 syndromes, 1,263 symptoms, and 2,485 relations. It incorporates a four-stage symptom matching pipeline (exact, semantic, fuzzy, and large language
The convergence of advanced AI, large language models, and specific domain knowledge (like traditional Chinese medicine) is now mature enough to be applied to complex diagnostic and treatment planning systems, pushed by demand for more transparent AI in healthcare.
This development showcases a tangible application of AI to enhance diagnostic accuracy and treatment personalization, particularly in under-served or complex medical fields, while addressing transparency concerns with explainable AI.
AI-assisted diagnostics and treatment planning become more transparent, interactive, and multimodal, moving beyond passive tools to integrated systems that can explain their reasoning and adapt to user input.
- · Healthcare providers (TCM)
- · AI healthcare technology companies
- · Patients seeking personalized treatment
- · Researchers in explainable AI
- · Traditional diagnostic tool manufacturers
- · Healthcare systems resistant to AI integration
- · Opaque AI diagnostic systems
Improved diagnostic accuracy and personalized treatment plans in TCM using multimodal AI interactions.
Increased adoption of AI in other complex medical disciplines, leading to a broader shift in clinical decision support systems.
Potential for sovereign AI development in healthcare, as nations seek to build domain-specific diagnostic systems for their unique cultural or medical practices, reducing reliance on foreign tech stacks.
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Read at arXiv cs.AI