Despite rapid advancements in artificial intelligence (AI) for medical imaging, widespread clinical adoption remains limited. In March 2025, the Academy for Radiology & Biomedical Imaging Research convened a cross-sector roundtable to examine operational and structural challenges in AI development and implementation. Researchers, department leaders, government representatives, and industry executives participated in a structured two-stage discussion using the AI lifecycle and a simplified failure modes and effects analysis (sFMEA) framework. In the first stage, attendees examined each phase of the AI lifecycle to identify domains where implementation barriers arise. In the second stage, mixed stakeholder groups applied a qualitative sFMEA approach to analyze process vulnerabilities within those domains and discuss mitigation approaches. This manuscript summarizes the session design, synthesizes key domains, and presents illustrative mitigation approaches across five areas: governance, use cases, implementation, cost, and regulation. The discussion identified recurring challenges related to fragmented priorities, infrastructure constraints, and regulatory complexity, as well as the need for clearer governance structures and more consistent evaluation processes to improve coordination across stakeholders.
Towbin et al. (Wed,) studied this question.