Features Highlights

AI Triaging

Classification of scans with and without suspicions at high precision

Driving the Future of Radiology with

 Responsible AI

C. Responsible AI

Build Trust, Measure RoI on AI, Enable Feedback

Cost Saving and productivity gain

Cost impact of automating normal scan reporting

Parameter Description Hospital A Hospital B Hospital C Hospital D
Precision (Normal) Out of all the cases predicted by Al as Normal, what percentage of them are actually Normal 99% 97% 97% 96%
False Discovery Rate(FDR) Out of all cases segregated by Al as normal, what percentage of them are actually abnormal 1% 3% 3% 4%
Reduction in workload Percentage of all scans that can be reported as normal by Al 63% 55% 37% 45%
Error rate (in clinical settings) Out of all scans predicted by radiologist as normal, what percentage of them were actually abnormal 8% 6% 8% 11%

Regulatory Approvals

US FDA Cleared

CDSCO Approved

Kenya Board Certified

Thai FDA Approved

Our Research

Improvement in reader sensitivity and chest radiograph interpretation time using DeepTek AI Platform - a dual center study of 4476 radiographs

Automated Detection and Quantification of the Severity of Lung Involvement in COVID-19 & community-acquired pneumonia

A deep-learning model for rapid quantification of cardiothoracic ratio (CTR)

Impact Stories

Genki leveraged at Greater Chennai Corporation for TB screening

Augmento becomes National AI platform for Singapore - adopted by Synapxe

Transforming radiology worklow at Maxicare Healthcare - Phillipines