arXiv:2607.08084v1 Announce Type: cross Abstract: Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models can be overconfident or poorly calibrated, making derived measurements appear more reliable than they are. Conformal prediction (CP) provides distribution-free prediction intervals with finite-sample marginal coverage guarantees, but black-box intervals for segmentation-derived radiomics can be inefficient because they
The paper addresses a critical issue in the increasing integration of AI in medical imaging interpretation, specifically regarding the reliability and calibration of AI-derived radiomic features.
Ensuring the robustness and trustworthiness of AI in medical diagnostics is crucial for clinical adoption and patient safety, impacting the credibility and utility of AI in healthcare.
Improved methods for quantifying uncertainty in AI predictions, particularly for radiomics, will enhance clinical decision support and potentially accelerate regulatory approval for AI in diagnostics.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Radiology departments
- · Developers of poorly calibrated AI models
Increased trust and adoption of AI-driven tools in medical diagnostics.
Faster innovation cycle for medical AI as models are held to higher standards of reliability.
Potential for new regulatory frameworks specifically designed for AI reliability in high-stakes applications like medicine.
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Read at arXiv cs.LG