AI Monitoring AI with LLMs: The American College of Radiology's Imaging AI Registry.
TL;DR
LLM-based extraction within Assess-AI enables scalable, report-anchored AI performance monitoring in radiology, and demonstrates feasible report-finding extraction at scale.
Abstract
Objective
To describe the technical workflow enabling scalable automated artificial intelligence (AI) monitoring in the first national imaging AI registry, Assess-AI.
Materials And Methods
Large language model (LLM) prompts are developed to extract clinically relevant findings from radiology reports through collaboration between data scientists and subspecialty radiologists. Prompts are optimized using tuning cohorts of use case-specific radiology reports and LLMs available through AWS Bedrock. Such cohorts are used to evaluate prompt accuracy and consistency across 10 repeated runs. Report-AI result pairs submitted to the Assess-AI registry for actively monitored use cases are additionally used to further optimize corresponding prompts.
Results
Prompts were developed for nine use cases. In Stage 2 development cohorts, final-prompt agreement with hybrid report-derived reference standard labels was 0.985 for ICH and 0.997 for PE. Because these cohorts informed prompt refinement and label construction, they were not independent validation sets.
Discussion
The workflow demonstrates feasible report-finding extraction at scale; independent accuracy and clinical utility remain unestablished.
Conclusion
LLM-based extraction within Assess-AI enables scalable, report-anchored AI performance monitoring in radiology.