Answers are grounded in this paper and cite their sources.
PRIME 2.0 provides an updated, domain-specific framework to standardize the development, evaluation, and reporting of multimodal AI in cardiovascular imaging.
The PRIME (Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation) 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to general AI reporting guidelines, it delivers detailed, practical recommendations on all critical aspects of AI research and builds upon the original 7-domain framework by incorporating cardiovascular imaging-specific complexities such as cardiac motion, imaging artifacts, and interobserver variability. By promoting transparency and rigor, PRIME 2.0 can serve as a vital resource for researchers, clinicians, peer reviewers, and journal editors working at the forefront of AI in cardiovascular imaging.
No takes yet. Share an insight, caveat, or question.
Kagiyama et al. (2025) studied Cardiovascular imaging AI evaluation. PRIME 2.0 checklist was evaluated. The PRIME 2.0 checklist provides an updated, domain-specific framework developed by an international panel to standardize the evaluation and reporting of AI applications in cardiovascular imaging.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: