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Agentic AI for Multimodal Medical Diagnosis: An Orchestrator Framework for Custom Explainable AI Models
mahasin-labs·
This paper presents a novel Agentic AI framework for multimodal medical diagnosis that integrates custom-developed Explainable AI (XAI) models specifically tailored for distinct clinical cases. The system employs an AI agent as an orchestrator that dynamically coordinates multiple verified diagnostic models including UBNet for chest X-ray analysis, Modified UNet for brain tumor MRI segmentation, and K-means based cardiomegaly detection. Each model has undergone rigorous clinical validation. Experimental results demonstrate 18.7% improvement in diagnostic accuracy, with XAI confidence scores reaching 91.3% and diagnosis time reduced by 73.3%.