AI is widely used in health systems as a tool for reducing clinicians' administrative burden. But that may only be the tip of the iceberg. What was primarily back office and front office is now moving into care delivery – we are now in the era of the AI doctor.
There’s a lot of talk about how radiology is a prime target for AI. But increasingly, industry experts view cardiology as the specialty most primed for disruption. Heart disease remains the leading cause of death globally.
“Cardiology is, in many ways, a flag-bearer for how the technologies could be used,” Dr. Joshua Lampert, an electrophysiologist and the medical director of Machine Learning for Mount Sinai Heart, tells Second Opinion, and adds that cardiology uniquely spans the “full spectrum of clinical care.”
The specialty is so well-positioned because it encompasses prevention and risk stratification for the development of heart disease, alongside inpatient management and follow-up care after a cardiac event. AI could be used across the full spectrum. Heart disease is also one of the longest-studied medical conditions in history, which means there’s plenty of data to feed AI algorithms.
There’s a reason that ARPA-H, the research and funding agency that supports biomedical breakthroughs, recently announced a program aimed at using AI to transform heart disease management. The agency is currently accepting applications for companies that can bring specialty care using agentic AI to millions of Americans who lack access to a cardiologist.
“Cardiology has more technology tools than virtually any other specialty,” Dr. David Albert, the founder and chief medical officer of AliveCor, a provider of AI-powered electrocardiogram (ECG) technology, tells Second Opinion. Dr. Albert notes that imaging has been digitized for over two decades, so there’s a strong precedent for the specialty to adopt new technologies.
Using existing diagnostic tools, clinicians and entrepreneurs are already experimenting with AI that can identify subtle signs of disease and patients at higher risk of disease progression — and provide analysis of wearable data. For its part, Alivecor already has on the market a personal electrocardiogram (ECG), as well as an alerts system called KardiaAlert that informs its 300,000 patient subscribers about potential changes to their ECG. Dr. Albert noted in the announcement that the system - dubbed KardiaMobile 6L Max - can identify up to 20 arrhythmias with a clinician review.
Editors’ Note: Alivecor.com is a sponsor of this analysis and overview of the AI and cardiology space.
“There are new mathematical approaches that are feasible now that weren't ten years ago for a variety of reasons,” Lampert adds, indicating that it's due to integrated electronic health records and improved machine learning capabilities. “What we can do now is actually make individual estimates of probabilities for a clinical outcome.”
Medicine’s Biggest Data Mine
Cardiology, one of medicine’s most technology-dense specialties, sits on a treasure trove of data.
“Cardiology is really a hotbed for where these deep learning tools are able to shine,” says Lampert. “Humans don't have the cognitive bandwidth in the modern era of medicine to assimilate all of this data and be able to quantify their uncertainty in any prediction.”
Dr. Jeffrey Wessler, a cardiologist at Northwell Health in New York, agrees and says the problem in cardiology isn’t a lack of data, but rather, too much of it. “These AI models require such a large amount of data across diverse populations to be effective, and cardiology was built for that,” he told us.