ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI
arXiv:2510.17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent 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 multi-agent 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, cl
The proliferation of AI in critical applications like medical imaging necessitates robust, automated monitoring and correction mechanisms to maintain performance and trust over time, especially as models encounter real-world data drift.
This development addresses a critical vulnerability in AI deployment, moving beyond initial model validation to ensure sustained reliability and safety, which is crucial for widespread adoption in sensitive fields.
The operational lifecycle of AI models will increasingly incorporate autonomous agents for dynamic performance management, shifting from reactive human oversight to proactive, automated maintenance.
- · Healthcare AI providers
- · Medical imaging diagnostics
- · AI assurance and governance platforms
- · Patients
- · AI models without continuous monitoring
- · Manual AI model maintenance services
- · Organizations prioritizing one-off AI deployments
Medical imaging AI systems become more robust and trustworthy due to continuous, automated performance monitoring and correction.
Increased adoption of AI in high-stakes medical diagnoses as concerns around performance degradation over time are mitigated.
The methodology for autonomous agent-based monitoring extends to other critical AI applications, accelerating trust and integration across various sectors.
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Read at arXiv cs.AI