2025/10/19 by Eleftherios Tzanis, Tzanis, Eleftherios, Michail E. Klontzas +1 · 1 voice
Computer Science · Medicine · #Adversarial Robustness in Machine Learning #Artificial Intelligence in Healthcare and Education #COVID-19 diagnosis using AI #Explainable Artificial Intelligence (XAI) #Radiomics and Machine Learning in Medical Imaging
paper · doi:10.1148/ryai.250923
openalex publication_date 2026/06/03 · openalex created_date 2026/06/04 · openalex updated_date 2026/06/04
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 performed 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 8 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. © RSNA, 2026.