Acquisition state behaves as a structured, measurable variable governing lung-nodule AI: kernel-driven measurement instability and noise-driven detection fragility, invisible to DICOM metadata
arXiv:2606.12824v1 Announce Type: cross Abstract: AI governance for medical imaging is formalizing: the 2026 ACR-SIIM Practice Parameter recommends local acceptance testing and ongoing drift monitoring, and the ACR Assess-AI registry monitors AI outputs using DICOM metadata for context. We argue that a necessary, currently unmonitored layer sits beneath output metrics: whether incoming studies remain within the acquisition envelope a model was validated on. Using a LUNA16-trained MONAI RetinaNet lung-nodule detector, we test whether acquisition state behaves as a structured, measurable variabl
The formalization of AI governance in medical imaging, exemplified by the 2026 ACR-SIIM Practice Parameter, is pushing for more robust validation and monitoring methods, creating an immediate need to address hidden variables affecting AI performance.
This research highlights a critical vulnerability in medical AI validation and ongoing performance monitoring, demonstrating that undetected variations in data acquisition can lead to significant diagnostic failures, undermining trust and efficacy.
The focus for medical AI governance will likely expand beyond just output metrics and DICOM metadata to include a deeper analysis and control over the 'acquisition envelope' to ensure AI model robustness and reliability.
- · AI robustness testing platforms
- · Medical AI governance bodies
- · Healthcare providers with advanced data quality controls
- · AI developers ignoring acquisition variability
- · Patients relying on inadequately validated AI
- · Regulators with superficial oversight mechanisms
Medical AI models will require more sophisticated pre-deployment validation and continuous monitoring strategies accounting for acquisition parameters.
Increased R&D into 'acquisition state' monitoring tools and robust-by-design AI models will become a priority.
The development and adoption of new, standardized metadata beyond DICOM, specifically for AI-relevant acquisition parameters, could emerge as a critical infrastructure requirement.
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Read at arXiv cs.AI