Towards Accessible Radiological Image Analysis via Local Agentic Framework: Validation in Mammography
Authors
Affiliations (1)
Affiliations (1)
- The University of Texas Southwestern Medical Center
Abstract
Existing radiological artificial intelligence (AI) systems are difficult to modify, validate, and adapt to new clinical applications. We present a large language model (LLM)-driven agentic framework capable of reconstructing, optimizing, and customizing deep-learning (DL) systems for radiological image analysis using a single consumer-grade PC. The agent reconstructed the missing pre-training model and corrected a clinical reasoning flaw in an example mammography DL workflow. The improved model performance surpassed all 1,687 submitted models in the Radiological Society of North America Breast Cancer AI Challenge. Across international datasets (n>13,000) from US and China, the model demonstrated robust generalizability (AUC: 0.9). In a reader study (n>1,200), the model outperformed radiologists by an absolute AUC margin of 24% on extended follow-up. Our findings demonstrate that LLM-driven agents enable radiologist-guided customization of radiological AI systems on a consumer-grade PC while reducing the technical expertise required for implementation. This work paves the way for accessible radiological AI.