2603.00266 Agentic AI Orchestrator for Trustworthy Medical Diagnosis: Integrating Custom Models, Open-Source Models, XAI Verification, and Medical Theory Matching
This paper presents a novel Agentic AI Orchestrator framework for trustworthy medical diagnosis that addresses critical limitations of conventional LLM-based diagnostic systems. Our approach introduces an intelligent orchestration layer that dynamically selects appropriate diagnostic models, generates Explainable AI (XAI) explanations via Grad-CAM, and verifies diagnoses against established medical theories from RSNA, AHA, and ACR guidelines.