AI’s Billion-Dollar Bet: Reimagining Cardiometabolic Prevention

The rising tide of cardiometabolic disease, type 2 diabetes, obesity, dyslipidemia, is a clinical and financial disaster. The old model of waiting for a heart attack and then reacting is failing. We’re finally getting the tools to shift from reactive care to proactive, data-driven prevention, and artificial intelligence (AI) is what’s making that possible by changing how we spot, track, and head off cardiometabolic risk.

The Algorithmic Underpinnings of Cardiometabolic Risk Prediction

To get ahead of cardiometabolic disease, you have to make sense of huge amounts of patient data and turn it into something a doctor can actually use. Companies like Tempus AI and Viz.ai are leading this charge. They work by pulling together and analyzing everything from multi-omic data and electronic health records (EHRs) to real-world evidence (RWE). Tempus AI, for example, has built its business on a massive “data moat” of genomic sequences, clinical notes, and treatment outcomes. Their AI models apply deep learning to find connections in this data that a human could never spot, like how a specific genetic marker, combined with certain metabolic results and a patient’s medication history, points to a high risk for metabolic syndrome. The result isn’t just a generic warning, but a specific probability of a cardiovascular event within a set timeframe or a personalized plan for early action. Tempus has already gotten FDA clearances for its AI-powered ECG software, first in 2024 to predict one-year risk of A-fib or flutter, and they’re projecting another in August 2026 for detecting pulmonary hypertension from standard ECGs. Their models, which often use recurrent neural networks (RNNs) or transformer architectures for handling patient histories, are showing biomarker correlation coefficients that are simply in a different league than old-school risk calculators. Peer-reviewed study on multi-omic predictive accuracy in cardiometabolic disease Viz.ai is better known for its work in acute stroke and pulmonary embolism, but it’s applying the same AI playbook to preventive cardiology (its healthcare business hit profitability in 2025). The Viz HCM module, which got De Novo approval from the FDA in August 2023, is designed to spot and flag patients with signs of hypertrophic cardiomyopathy. Their system often works by analyzing medical images, like CT scans or echos, and cross-referencing them with clinical data to find incidental red flags. For instance, an AI can scan a routine abdominal CT and automatically measure visceral adipose tissue, a key risk factor for insulin resistance that might have otherwise gone unnoticed. Finding coronary artery calcification on a non-cardiac CT is another classic example. The AI flags it, connects it to other data in the patient’s file, and builds a complete risk profile on the spot. Spotting these things early allows for immediate intervention, which is how you make guideline-adherent care the default standard. To push this clinical strategy forward, Viz.ai even hired its first Chief Medical Officer, Tim Showalter, in February 2026.

Clinical Validation and Guideline Formulation Through Registry Studies

An AI platform’s algorithm is worthless if it doesn’t work in the real world, and its credibility is tied directly to validation in large-scale registry and observational studies. You have to prove that these models are effective and generalizable outside the clean confines of a clinical trial. The whole “Guideline Formulation” concept rests on this. The AI has to either help doctors stick to existing care guidelines or generate the evidence needed to write better ones. A high area under the receiver operating characteristic curve (AUROC) in a test set is table stakes. The real test is showing that AI-driven insights actually improve patient outcomes in a chaotic clinical setting. This is where large registries with data from hundreds of thousands or even millions of patients become the perfect testing ground. You can retrospectively apply an AI model to the data and see if its risk predictions line up with what actually happened to patients over the years. For example, a research team could take Tempus AI’s diabetes risk model and apply it to a national cardiometabolic registry. Then they could track how many patients followed the AI’s recommendations for lifestyle changes or drugs and, more importantly, compare the rate of new diabetes cases in the AI-guided group to a control group that got standard care. These are the kinds of studies that generate the real-world evidence (RWE) payers and hospitals need to see before they’ll adopt and reimburse the technology. Even something as tricky as patient adherence, a notoriously weak spot in prevention, can be tracked and improved with AI-driven personalized nudges, and the effect can be measured in these large observational studies. The acquisition of Paige AI by Tempus in August 2025, while focused on oncology pathology, shows the power of this approach. Paige’s AI trains on huge libraries of digitized pathology slides to find subtle visual patterns that predict disease. While not their current focus, you could imagine a similar AI analyzing adipose tissue biopsies to find new markers for cardiometabolic risk. It’s all about using AI to pull out previously invisible prognostic features from complex biological data.

Bridging the Gap to Personalized Preventive Therapy

In the end, the point of using AI in cardiometabolic prevention is to make it deeply personal. The AI’s real power is its ability to go beyond broad, population-level risk scores and drill down to an individual patient. This happens in a few ways:

• Dynamic Risk Stratification: The AI doesn’t just calculate risk once. It’s constantly re-evaluating as new data comes in, whether it’s fresh lab results, data from a wearable, or a change in prescriptions. This is the only way to counter algorithmic drift and keep the model’s predictions relevant as a patient’s condition changes over time.
• Targeted Interventions: Instead of giving everyone the same generic advice, the AI recommends specific, evidence-based actions. This could be a diet change, a new exercise plan, or a prescription for a GLP-1 agonist or SGLT2 inhibitor, all based on that person’s specific risk profile and how the model predicts they’ll respond. It’s the difference between a pamphlet and a plan.
• Prognostic Enrichment: The AI can pinpoint the small group of patients who are quietly heading for a cliff and who would benefit most from intensive, early intervention. This lets clinics focus their resources where they’ll have the biggest impact, which is especially important for diseases with long, silent asymptomatic phases. Getting these insights into a doctor’s daily workflow is the hard part. It requires Software as a Medical Device (SaMD) solutions that plug directly into the hospital’s EHR, feeding clinicians clear and actionable advice right at the point of care. Getting regulatory approval for these tools, typically through a 510(k) or De Novo path, requires exactly the kind of strong clinical evidence and adherence to Good Machine Learning Practice (GMLP) that these registry studies provide. FDA framework for AI/ML-based medical devices
  

  Methodology Note: The Imperative of Registry-Based Research

For any cardiology researcher or medical journalist covering this space, the reliance on large-scale registry and observational studies for credibility is the main story. Randomized controlled trials (RCTs) are still the gold standard for proving a drug works, but they are slow, expensive, and can’t capture the sheer messiness of real-world patient populations and long-term outcomes. Registry studies, however, are perfectly suited for validating AI in cardiometabolic prevention for a few reasons:

• Ecological Validity: They show how the AI performs in the wild, with real patients who have multiple health problems, take lots of different drugs, and don’t always follow doctor’s orders.
• Scale and Diversity: A registry can provide data on millions of patients from all over the country, which helps prove that an AI model works for everyone, not just one specific group.
• Longitudinal Data: Because these registries track people for years, you can see if the AI’s predictions about long-term health actually come true and if the early interventions it suggested made a difference.
• Ethical Considerations: Looking back at existing data is usually much simpler from an ethical standpoint than setting up a new, long-term interventional trial, which means AI models can be tested and improved much faster. Of course, the challenge with observational data is always managing its quality, dealing with missing information, and accounting for confounding variables. That’s why solid statistical methods and transparent reporting, using standards like the Guidelines for reporting observational studies in epidemiology, are absolutely essential to make the findings trustworthy. The work being done by AI companies like Tempus AI and Viz.ai is a fundamental change in our approach to medicine. By using powerful algorithms to analyze huge datasets and uncover the hidden mechanics of disease, they’re creating a new kind of cardiology that is more predictive, personal, and proactive. But this revolution depends entirely on rigorous, scientific validation through large-scale registry studies to prove that these AI-driven insights lead to better care and align with the highest standards of medicine.