aceExplainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations
Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs…
Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust.
Evidence
- Peer-reviewedDiagnostic and Interventional Radiology2026-08-21
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Truvace Impact Record TRV-2026-0847, v1: “Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations.” Truvace, 2026-08-22. /record/TRV-2026-0847 (accessed at citation time). sha256 740b593f8df3d9b1…
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