Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.
Authors
Affiliations (7)
Affiliations (7)
- University of Maryland-Institute for Health Computing (UM-IHC), North Bethesda, Md.
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201.
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Conn.
- Radiology Partners, Nashville, Tenn.
- Department of Radiology, New York Langone Health/Grossman School of Medicine, New York, NY.
- Department of Radiology, University of Connecticut Health Center, Farmington, Conn.
- Department of Radiology, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio.
Abstract
Artificial intelligence (AI) has progressed from technical research to routine clinical use, reaching an inflection point where technological capabilities may exceed current regulatory and oversight frameworks. These systems are becoming more complex, progressing from narrow, task-specific algorithms to foundation models and early agentic prototypes. This progression has redistributed risk, responsibility, and clinical judgement, requiring radiologists and health care leaders to understand how policy choices affect patient safety and clinical innovation advancement. This Special Report provides a roadmap for aligning policy with clinical practice through a practical, lifecycle-based framework centered on patient safety. "Translational bialignment" is a concept which pairs regulatory science requirements (what AI systems deliver to clinicians and patients) with implementation science capabilities (what institutions provide to AI for safe deployment). This framework addresses the full AI lifecycle, from data stewardship and model development to validation, deployment, and monitoring, and articulates shared responsibilities for vendors, institutions, and clinicians grounded in trustworthy AI principles. The analysis focuses on U.S. regulatory frameworks, particularly Food and Drug Administration policies governing medical AI, with relevant highlights from international approaches. Concrete opportunities for radiologists to engage in policy formation, participate in oversight, and collaborate with industry and policymakers are provided to help shape a trustworthy and sustainable AI ecosystem. The alignment of policy, practice, and patient safety will enable medical AI to have a lasting impact on clinical care and public trust. The analysis and recommendations provided represent the authors' perspectives and do not necessarily reflect the official positions of the Radiological Society of North America.