{"ID":2853255,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17004","arxiv_id":"2510.17004","title":"ReclAIm: A multi-agent framework for degradation-aware performance tuning of medical imaging AI","abstract":"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.","short_abstract":"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 cl...","url_abs":"https://arxiv.org/abs/2510.17004","url_pdf":"https://arxiv.org/pdf/2510.17004v1","authors":"[\"Eleftherios Tzanis\",\"Michail E. Klontzas\"]","published":"2025-10-19T21:02:01Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}