AI Outperforms Doctors in Cardiac Prediction

by Alex Jordan, InnerSelf.com

Sudden cardiac death (SCD) doesn’t give second chances. It often strikes without warning and accounts for a staggering number of fatalities worldwide—between 50 and 100 out of every 100,000 people in North America and Europe annually. While implantable defibrillators can prevent these tragedies, the real challenge lies in knowing who actually needs them. That’s where medicine has historically faltered—especially in patients with hypertrophic cardiomyopathy (HCM), an inherited condition often afflicting the young and seemingly healthy.

Doctors have leaned on guidelines based on ejection fractions—how much blood the heart pumps out with each beat. But HCM patients don’t usually have low ejection fractions. Their hearts might even be hyperactive. So the red flags simply aren’t red enough. And when traditional tools miss, patients pay the ultimate price.

Introducing MAARS: A Smarter Predictor

Developed by a team at Johns Hopkins University, the Multimodal Artificial Intelligence for ventricular Arrhythmia Risk Stratification—mercifully shortened to MAARS—doesn’t just look at one aspect of patient health. It learns from everything: electronic health records (EHRs), echocardiogram reports, contrast-enhanced MRI images, and more. The model uses transformer-based deep learning, a next-gen neural network architecture similar to what powers AI like ChatGPT or image recognition tools.

The breakthrough lies in how MAARS processes this information. Instead of taking doctors' interpretations of MRIs, it reads the raw scan data. That means no filtering by human eyes, no bias, no oversight. It identifies patterns in fibrosis—the scarring inside the heart—that a radiologist might overlook. And it does this in 3D using a Vision Transformer (3D-ViT), retaining all the complexity of the actual human heart.

Outperforming the Experts—By a Lot

Let’s talk results. In testing against standard clinical tools—the ACC/AHA guidelines, ESC risk scores, and the HCM Risk-SCD calculator—MAARS didn’t just edge out the competition. It crushed them. In the internal validation cohort, MAARS reached an Area Under the Curve (AUC) of 0.89. Clinical tools hovered between 0.54 and 0.62. In external testing from a different hospital system, MAARS still held strong with an AUC of 0.81—far higher than anything doctors currently use.

That’s not a marginal improvement. It’s a game-changer. For context, an AUC of 0.5 is the same as flipping a coin. The leading tools barely rise above that threshold. MAARS doesn't just predict better—it predicts accurately and consistently across age groups, sexes, and races.

Bias in Medicine: A Problem AI Might Fix

Fairness isn’t a buzzword here—it’s life or death. Medical tools often fail minorities and younger patients due to limited trial data or skewed assumptions. But MAARS, built on a multimodal framework, showed remarkably even performance across subgroups. Whether the patient was young or old, male or female, African American or white, MAARS performed with near-equal accuracy. That’s rare in clinical prediction—and vital in a world of widening health disparities.

One surprising twist? African American ethnicity was actually correlated with decreased SCDA risk in the model—a result that warrants deeper exploration but also hints at the nuanced insight AI can provide, especially when we let it speak from raw data instead of human assumptions.

Transparency in a Black Box

Most people don't trust black-box algorithms—and rightly so. MAARS doesn’t just spit out a risk score; it explains itself. Using techniques like Shapley values and attention mapping, the model reveals which factors influenced its decision. Was it a history of atrial fibrillation? A stress test showing lower heart-rate response? Or hidden patterns in fibrosis on a cardiac scan? MAARS doesn’t leave clinicians guessing. It gives them a roadmap to understand the risk—and potentially, the underlying pathology.

This isn’t interpretability as a feature. It’s interpretability as a responsibility. When AI is making life-altering recommendations, especially about who gets an implanted defibrillator, clarity is essential. MAARS provides it, visually and statistically.

Why Raw Medical Data Changes the Game

There’s a lesson here that goes beyond cardiology: raw data beats summaries. Doctors’ reports, while invaluable, introduce subjectivity. But MAARS reads the signal itself—the actual scan—and learns patterns that no human has taught it to see. It’s not limited by what we already think is important. In doing so, it redefines what “important” even means in the clinical setting.

This shift, from interpreting pre-processed data to analyzing raw inputs, is central to the next wave of medical AI. It moves from mimicking doctors to augmenting—or surpassing—them. It’s the difference between training a parrot and raising a diagnostician.

Limitations and Real-World Barriers

Let’s not paint MAARS as flawless. Like all models, it faces challenges. Its training cohorts were relatively small—just over 800 patients in total—and sudden cardiac death remains a rare event. That means limited data points for what the model is ultimately trying to predict. While the algorithm performed well in both internal and external validation, it will need testing across broader populations and longer timelines.

Another hurdle? The infrastructure needed. Not all hospitals have the imaging hardware, data processing pipelines, or personnel to implement such a system. Yet as data sharing, cloud storage, and AI-assisted diagnostics evolve, MAARS-like models may become far more accessible—even in smaller clinics or developing regions.

Redefining Accountability and Clinical Judgment

Here's the uncomfortable question: what happens when a machine sees what your doctor misses? Do we trust the model? Or do we fall back on the safety of human judgment? MAARS pushes that boundary. It doesn’t replace doctors—it challenges them to think differently, to integrate data they might not have time to analyze fully, and to rely on tools that aren’t constrained by sleep, stress, or clinical intuition.

The future isn’t man vs. machine. It’s man with machine. And when it comes to preventing one of the most sudden and tragic causes of death, that partnership could be priceless.

MAARS might be just one acronym in the alphabet soup of medical AI, but its implications go far beyond cardiology. It tells us something vital about the future of care: the smartest diagnosis may come not from what you see, but from what you finally decide to trust.

About the Author

Alex Jordan is a staff writer for InnerSelf.com

Article Recap

MAARS is a multimodal AI model that predicts cardiac arrest more accurately than doctors by analyzing raw imaging and medical data. It delivers fairer, more transparent, and highly personalized risk assessments in hypertrophic cardiomyopathy. By outperforming traditional tools and reducing bias, MAARS signals a major leap forward in cardiac prediction and AI-driven healthcare.

#cardiacprediction #AIhealthcare #MAARSmodel #suddencardiacdeath #heartdisease #artificialintelligence #medicalAI #cardiovascularcare #healthtech #echocardiogram #CMRimaging

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