Cardiology, in particular, offers a clear view into why so many AI solutions struggle to scale and what differentiates those that are poised for adoption. Across healthcare, AI-enabled diagnostic solutions are flooding the market. From cardiology and imaging to oncology and primary care, clinicians are being asked to evaluate algorithms that promise earlier detection, better accuracy, and more efficient care. Many of these tools are technically impressive. Some are FDA-cleared. A few even come with early clinical data. Yet only a relatively small number have become part of routine clinical practice.
The Real Test for AI Diagnostics Isn’t Performance — It’s Clinical Adoption
Related Articles
Profile of a Founder: Venky Soundarajan
The Tech Tribune: An exclusive Tech Tribune Q&A with Venky Soundararajan (co-founder and CSO) of nference, which was honored in our: 2022, 2021, 2020 Best Tech Startups in Cambridge.April 14, 2021
Tackling Imbalanced Regression in Clinical AI with KDE-weighted Deep Models
Clinical AI systems often include automated analysis of medical time series, such as electrocardiogram (ECG), to serve as valuable diagnostic decision support tools. While many cardiovascular diseases are traditionally measured…June 15, 2025
AI algorithm for detection of cardiac amyloidosis gains FDA breakthrough device status
Healio: Anumana announced it received FDA breakthrough device designation for its artificial intelligence-guided ECG algorithm for the early identification of cardiac amyloidosis.June 21, 2023
How Doctors Use AI to Help Diagnose Patients
Wall Street Journal: At Mayo cardiology, an AI tool has helped doctors diagnose new cases of heart failure and cases of irregular heart rhythms, which are called atrial fibrillation, potentially…February 28, 2023


