PRIME 2.0: An Update to The Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation Checklist

By afschroeder • September 3, 2025

Kagiyama N, Tokodi M, Hathaway QA, Arnaout R, Davies R, Dey D, Duchateau N, Fraser AG, Goto S, Jamthikar AD, Lam CSP, Oikonomou EK, Ouyang D, Pandey A, Poterucha TJ, Raisi-Estabragh Z, Strom JB, Zhang Q, Yanamala N, Sengupta PP. PRIME 2.0: An Update to The Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation Checklist. JACC Cardiovasc Imaging. 2025 Aug 18:S1936-878X(25)00466-8. doi: 10.1016/j.jcmg.2025.08.004. Epub ahead of print. PMID: 40892627.

Tags: Artificial intelligence, biomedical sciences, Cardiology/Epi, cardiovascular imaging, Cedars Sinai DoM, Clinical Validation, Deep learning, Large language models, Model Development, Multimodal Generative AI, PRIME 2.0 Checklist, Publications, Transparency and Reproducibility

Related Posts

FAP Expression in Renal Tumors Assessed by [68Ga]Ga-FAPI-46 PET Imaging and FAP Immunohistochemistry: A Case Series of Six Patients from the Prospective Exploratory Trial NCT04147494

Holzgreve A, Unterrainer LM, Flores K, Lam EC, Mona CE, Czernin J, Shuch BM, Sisk AE Jr, Calais J. FAP Expression in Renal Tumors Assessed[...]

Akebia Saponin D Targeting Ubiquitin Carboxyl-Terminal Hydrolase 4 Promotes Peroxisome Proliferator-Activated Receptor Gamma Deubiquitination and Activation of Brown Adipose Tissue Thermogenesis in Obesity

Chen L, Liu DH, Li YX, Yang S, Jia WH, Peng L, Liu HL, Wang XB, Hu B, Wang YC, Pan C, Lusis AJ, Liu[...]

The relationship between blood eosinophil count and cardiovascular outcomes in prediabetes and diabetes patients with acute coronary syndrome: insights from a large Asian cohort analysis

Feng X, Liu Y, Qin H, Zhao S, Yang J, Yang S, Zhou Z, Liu Y, Wang Z, Shi D, Zhou Y, Guo Q. The[...]