Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology

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 judgment, 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 that pairs regulatory science requirements (what AI systems should deliver to clinicians and patients) with implementation science capabilities (what institutions should provide for safe deployment of AI). 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. Keywords: Artificial Intelligence, Food and Drug Administration, Health Policy, Implementation Science, Large Language Models, Machine Learning, Patient Safety, Regulatory Science, Translational Bialignment © RSNA, 2026.