The Road Map for ACR Practice Accreditation for Radiology Artificial Intelligence.
Journal:
Journal of the American College of Radiology : JACR
PMID:
40057886
Abstract
As the use of artificial intelligence (AI) continues to grow in radiology, it has become clear that its real-world performance often differs from that demonstrated in premarket testing, underscoring the need for robust quality management (QM) programs at local institutions. For decades, a key mechanism to ensure QM in radiology practices has been ACR accreditation. However, no such program currently exists for AI in radiology. As leaders of the ACR Commissions on Quality and Safety and Informatics, we are dedicated to establishing ACR accreditation for radiology AI. In this article, we outline our plan for this effort. ACR accreditation is a peer-reviewed process that evaluates radiology practices according to ACR Practice Parameters and Technical Standards, which are consensus-based guidelines aimed at improving care quality and reducing variability. ACR Practice Parameters focus on clinical aspects like patient management, and Technical Standards address the performance of imaging and treatment equipment. To support the development of this accreditation program, the ACR Recognized Center for Healthcare-AI (ARCH-AI) program has been established as a precursor to formal accreditation. ARCH-AI participants attest to meeting minimum criteria in areas such as governance, model selection, acceptance testing, monitoring, and management of locally developed models. Insights gained from ARCH-AI will inform the development of the formal accreditation program, which will culminate in ACR Council approval, currently anticipated in spring 2027. The College remains committed to fostering dialogue among members and stakeholders to ensure AI fulfills its promise of enhancing patient care safely and effectively.