ReclAIm: A multi-agent framework for degradation-aware performance tuning of medical imaging AI
This addresses the need for automated, user-friendly maintenance of medical imaging AI models to facilitate broader adoption in research and clinical environments, representing a novel method for a known bottleneck.
The paper tackles the problem of ensuring long-term reliability of AI models in clinical practice by introducing ReclAIm, a multi-agent framework that autonomously monitors, evaluates, and fine-tunes medical image classification models, achieving performance recovery within 1.5% of initial results after drops of up to -41.1%.
Ensuring the long-term reliability of AI models in clinical practice requires continuous performance monitoring and corrective actions when degradation occurs. Addressing this need, this manuscript presents ReclAIm, a multi-agent framework capable of autonomously monitoring, evaluating, and fine-tuning medical image classification models. The system, built on a large language model core, operates entirely through natural language interaction, eliminating the need for programming expertise. ReclAIm successfully trains, evaluates, and maintains consistent performance of models across MRI, CT, and X-ray datasets. Once ReclAIm detects significant performance degradation, it autonomously executes state-of-the-art fine-tuning procedures that substantially reduce the performance gap. In cases with performance drops of up to -41.1% (MRI InceptionV3), ReclAIm managed to readjust performance metrics within 1.5% of the initial model results. ReclAIm enables automated, continuous maintenance of medical imaging AI models in a user-friendly and adaptable manner that facilitates broader adoption in both research and clinical environments.