ReclAIm: A Multiagent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI

Purpose To develop and evaluate a multiagent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline in medical image classification models. Materials and Methods ReclAIm is a large language model-based multiagent system that operates through natural language interaction. A master agent coordinating three task-specific agents executed performance evaluation and triggered fine-tuning when substantial performance declines were detected. The fine-tuning workflow incorporated data augmentation, class imbalance handling, and a parameter-anchoring regularization strategy to limit catastrophic forgetting. The system was benchmarked using multiple imaging datasets, including brain MRI, chest CT, and chest radiography, partitioned into model development, inference (performance monitoring), and fine-tuning subsets (60%:20%:20%). Results ReclAIm successfully orchestrated training, evaluation, and performance monitoring across all datasets. Performance discrepancies between test and inference data were detected in eight of 18 models, prompting fine-tuning workflows that reduced performance gaps. In cases with declines of up to 40.6% (cardiomegaly dataset, InceptionV3), fine-tuning restored performance metrics to within ±2% of baseline values. Conclusion ReclAIm provides a prototype framework for automated monitoring and targeted fine-tuning of medical image classification models, with a natural language interface designed to support accessibility in research and potential clinical applications. Keywords: Diagnosis, CT, MR Imaging, Conventional Radiography, Convolutional Neural Network, Agents, AI, Continual Learning, Medical Image Classification, Large Language Model, Fine-tuning, Postmarket Surveillance, Monitoring, Performance Supplemental material is available for this article. © RSNA, 2026.