Health AI Central
Peer-Reviewed Publication
AJR Am J Roentgenol2026;1-8.August 26, 2026Journal Article

Explainable Artificial Intelligence (AI) for Medical Imaging: A Framework for Bridging the AI Trust Gap.

Cody H Savage1, Jeremias Sulam2,3, Chien-Ming Huang2,4, Paul H Yi4,5
1Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD.
2Department of Computer Science, Johns Hopkins University, Baltimore, MD.
3Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD.
4Malone Center for Engineering in Health Care, Johns Hopkins University, Baltimore, MD.
5Department of Radiology, St. Jude Children's Research Hospital, 262 Danny Thomas Pl, MS 220, Rm I3109, Memphis, TN 38105-3678.
PubMed DOI

Abstract

Artificial intelligence (AI) is increasingly used in health care but often lacks clinician and patient trust. Explainable AI (XAI) aims to clarify predictions and to make AI decisions more transparent, interpretable, and clinically actionable. However, current methods fall short. In this Perspective, we argue that for XAI to be clinically useful in medical imaging and to build trust with clinician…

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