An autonomous agent for auditing and improving the reliability of clinical AI models

2025-07-08

AI Summary

Clinical AI models often achieve strong benchmark performance yet suffer catastrophic failures under real-world distribution shifts—e.g., across imaging scanners, patient populations, or clinical environments—while existing reliability auditing methods remain labor-intensive, costly, and lack interpretability. To address this, we propose ModelAuditor, the first self-reflective multi-agent architecture for automated, clinically grounded distribution shift simulation, interpretable audit reporting, root-cause localization, and actionable remediation recommendations. It integrates task-adaptive metric selection, lightweight distribution simulation, and collaborative reasoning to enable low-cost, high-efficiency auditing on consumer-grade hardware (<$0.5 and <10 minutes per audit). Evaluated across three real-world clinical scenarios, ModelAuditor successfully identifies model-specific failure modes in state-of-the-art systems, recovers 15–25% of degraded performance, and substantially enhances robustness and trustworthiness for clinical deployment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The deployment of AI models in clinical practice faces a critical challenge: models achieving expert-level performance on benchmarks can fail catastrophically when confronted with real-world variations in medical imaging. Minor shifts in scanner hardware, lighting or demographics can erode accuracy, but currently reliability auditing to identify such catastrophic failure cases before deployment is a bespoke and time-consuming process. Practitioners lack accessible and interpretable tools to expose and repair hidden failure modes. Here we introduce ModelAuditor, a self-reflective agent that converses with users, selects task-specific metrics, and simulates context-dependent, clinically relevant distribution shifts. ModelAuditor then generates interpretable reports explaining how much performance likely degrades during deployment, discussing specific likely failure modes and identifying root causes and mitigation strategies. Our comprehensive evaluation across three real-world clinical scenarios - inter-institutional variation in histopathology, demographic shifts in dermatology, and equipment heterogeneity in chest radiography - demonstrates that ModelAuditor is able correctly identify context-specific failure modes of state-of-the-art models such as the established SIIM-ISIC melanoma classifier. Its targeted recommendations recover 15-25% of performance lost under real-world distribution shift, substantially outperforming both baseline models and state-of-the-art augmentation methods. These improvements are achieved through a multi-agent architecture and execute on consumer hardware in under 10 minutes, costing less than US$0.50 per audit.

Problem

• Auditing clinical AI models for real-world reliability failures
• Identifying and mitigating hidden failure modes in medical imaging
• Improving model performance under diverse clinical distribution shifts

Innovation

• Self-reflective agent for reliability auditing
• Simulates clinically relevant distribution shifts
• Multi-agent architecture for performance recovery