What if a routine mammogram could do more than screen for breast cancer? What if that same image could quietly reveal a woman’s future risk of heart disease—without extra tests, appointments, or burden on patients?

In this episode, we explore a large-scale study that uses deep learning to uncover cardiovascular risk hidden inside standard breast imaging. By analyzing mammograms that millions of women already receive, researchers show how a single scan can deliver a powerful second insight for women’s health. Laura brings the clinical perspective, unpacking how cardiovascular risk actually shows up in practice—from atypical symptoms to prevention decisions—while Vasanth walks us through the AI system that makes this dual-purpose screening possible.

We begin with the basics: how traditional cardiovascular risk tools like PREVENT work, what data they depend on, and why—despite their proven value—they’re often underused in real-world care. From there, we turn to the mammogram itself. Features such as breast arterial calcifications and subtle tissue patterns have long been linked to vascular disease, but this approach goes further. Instead of focusing on a handful of predefined markers, the model learns from the entire image combined with age, identifying patterns that humans might never think to look for.

Under the hood is a survival modeling framework designed for clinical reality, where not every patient experiences an event during follow-up, yet every data point still matters. The takeaway is striking: the imaging-based risk score performs on par with established clinical tools. That means clinicians could flag cardiovascular risk during a test patients are already getting—opening the door to earlier conversations about blood pressure, cholesterol, diabetes, and lifestyle changes.

We also zoom out to the bigger picture. If mammograms can double as heart-risk detectors, what other routine tests are carrying untapped signals? Retinal images, chest CTs, pathology slides—each may hold clues far beyond their original purpose. With careful validation and attention to bias, this kind of opportunistic screening could expand access to prevention and shift care further upstream.

If this episode got you thinking, share it with a colleague, subscribe for more conversations at the intersection of AI and medicine, and leave a review telling us which everyday medical test you think deserves a second life.

Reference:

Predicting cardiovascular events from routine mammograms using machine learning
Jennifer Yvonne Barraclough
Heart (2025)

Credits:

Theme music: Nowhere Land, Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0
https://creativecommons.org/licenses/by/4.0/