FDA Clears Radiology AI Enterprise Imaging Platform from CARPL.ai
Offering access to over 110 AI applications, the enterprise imaging platform enables radiologists to test, deploy and monitor the use of AI technologies.
The Food and Drug Administration (FDA) has granted 510(k) clearance for the CARPL.ai enterprise imaging artificial intelligence (AI) platform, a development that may help streamline the adoption of AI into radiology workflows.
CARPL.ai noted the platform allows radiologists access to over 110 AI applications from over 50 vendors with a single user interface. The platform facilitates worklist triage, segmentation, clinical audits, and auto-populated reports, according to the company.
Noting the adaptable capability of the platform for on-site or cloud-based hosting, CARPL.ai emphasized the potential for rapid scaling with no incremental costs for piloting of AI applications.
Vijay Rao, M.D., FACR, a senior vice president of enterprise radiology and imaging ay Jefferson Health in Philadelphia, added that the FDA clearance of the CARPL.ai platform allows for PACS integration of FDA-cleared AI applications that don’t have separate FDA-cleared viewers.
“This FDA clearance opens the door for widespread adoption of AI solutions through a platform approach which simplifies the process of selection, implementation, and procurement of AI by health systems,” noted Vidur Mahajan, the chief executive officer at CARPL.ai.
Meta-Analysis Shows Merits of AI with CTA Detection of Coronary Artery Stenosis and Calcified Plaque
April 16th 2025Artificial intelligence demonstrated higher AUC, sensitivity, and specificity than radiologists for detecting coronary artery stenosis > 50 percent on computed tomography angiography (CTA), according to a new 17-study meta-analysis.
The Reading Room Podcast: Emerging Trends in the Radiology Workforce
February 11th 2022Richard Duszak, MD, and Mina Makary, MD, discuss a number of issues, ranging from demographic trends and NPRPs to physician burnout and medical student recruitment, that figure to impact the radiology workforce now and in the near future.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
