Cardiac AI: The Billion-Dollar Plaque Analysis Opportunity

The whole game in cardiovascular diagnostics is changing. We’re finally moving away from just looking at function and starting to get a precise anatomical picture of atherosclerotic disease. Thanks to huge leaps in non-invasive imaging and the raw power of AI, we’re not just tweaking old workflows. We’re building entirely new markets. For any early-stage VC or clinical software dev, you have to understand what’s happening at this intersection to see where the real money will be made.

The Rise of Anatomical Plaque Assessment

For decades, evaluating coronary artery disease (CAD) meant putting someone on a treadmill for a stress test to find major blood flow blockages. That’s fine for spotting advanced disease, but it completely misses the slow, quiet buildup of atherosclerosis, the plaque accumulation inside the artery walls that’s the real trigger for most heart attacks. A whole new market is popping up right where non-invasive coronary imaging meets machine learning, focused on automating the measurement of this arterial plaque. This is a fundamental change in how we think about heart attack risk, moving from just “is there a blockage?” to “how much and what kind of plaque is there?” The American College of Cardiology (ACC) has been pushing this change, with their updated guidelines now pointing more and more doctors toward coronary CT angiography (CCTA) for chest pain workups ACC/AHA Joint Guidelines for the Evaluation and Diagnosis of Chest Pain. That ACC endorsement gives CCTA a solid clinical footing, since it gives you an incredible non-invasive look at the coronary arteries and can even characterize the plaque. The big problem? Reading these scans manually is a slow, subjective nightmare. And that’s exactly the gap AI-driven tools are now filling.

Vertical AI Specialization: Deepening Diagnostic Accuracy

The real muscle of AI here is its capacity to chew through huge imaging datasets with incredible speed and precision, turning a qualitative eyeball test into hard quantitative analysis. This kind of deep focus is what separates the winning vertical AI healthcare companies, especially in a field like cardiac prevention. Instead of trying to be a jack-of-all-trades, disease-specific AI platforms are just plain better in terms of performance and what they offer a clinician. Cleerly and HeartFlow are two perfect examples of this specialization in action:

• Cleerly: This company is AI-native, meaning it was built from the ground up to do one thing: total plaque analysis from CCTA scans. Its machine learning algorithms automatically segment and measure all the different plaque types, non-calcified, the dangerous low-attenuation stuff, and calcified plaque, to give a truly objective report on a patient’s atherosclerotic burden. This detail lets a cardiologist get way beyond a simple stenosis percentage and actually understand a patient’s risk based on their specific plaque makeup, a huge step forward for preventive care. Their whole business is built on a data moat of proprietary, labeled CCTA scans, which is what allows their algorithms to be so accurate.
• HeartFlow: HeartFlow also uses CCTA scans, but its AI does something different: it calculates fractional flow reserve (FFR). This tech, called CT-FFR, generates a non-invasive estimate of how much a specific stenosis is actually affecting blood flow, something that used to require sticking a catheter inside the patient. They’ve walled off this part of the market with a thicket of patents around CT-FFR, giving them a serious competitive edge. Their repeated FDA 510(k) clearances for their software prove that these specialized AI tools are commercially and regulatorily sound.

These two companies show what’s possible when an AI health platform goes deep instead of wide. They’re creating brand new diagnostic capabilities that we either couldn’t get before or had to get through an invasive procedure. Their success is a direct result of that vertical focus, which lets them build incredibly specialized algorithms, protect their IP with data and patents, and get through the FDA’s complex approval gauntlet.

Market Size, Reimbursement, and Regulatory Dynamics

The market for this kind of automated plaque analysis and physiological assessment is getting big, fast. As CCTA becomes a standard first-line diagnostic, the need for AI to help interpret the scans is going to explode. The total addressable market for cardiac AI is expected to jump from about $1.7 billion in 2025 to a whopping $14.8 billion by 2033, and these specialized diagnostic tools will be a huge piece of that pie. For anyone doing automated plaque analysis, the Centers for Medicare and Medicaid Services (CMS) is the entity that matters most. The good news is that getting paid just got a lot clearer with the new Category I CPT code 75577 for quantitative plaque assessment, which goes into effect on January 1, 2026. That new code replaces the old, less certain Category III codes and builds a solid road for reimbursement. Because of this, payers like Medicare and the big commercial insurers (Aetna, UnitedHealthcare, Cigna) are now covering the technology. Getting favorable reimbursement is probably the single biggest predictor of commercial success for these Software as a Medical Device (SaMD) solutions CMS CPT code registries for plaque analysis. You also have to de-risk the regulatory side. It’s not optional. Both Cleerly and HeartFlow have gotten their products through the FDA’s 510(k) clearance process, proving their software is safe and effective. Getting that FDA clearance is a massive milestone for any vertical AI health company. And it’s getting even more interesting with the FDA’s Predetermined Change Control Plan (PCCP) framework (final guidance was issued December 2024, with an update in August 2025). This lets companies make pre-approved modifications to their AI models without having to go through a full new submission every time they want to improve the algorithm. Cleerly, for example, already had a PCCP cleared with a recent 510(k).

Deploying Capital in Non-Invasive Coronary Diagnostics

For VCs and software developers looking at this space, the opportunities in non-invasive coronary diagnostics are obvious. The whole field is moving from functional testing to anatomical plaque assessment, pushed by clinical guidelines and made possible by AI, creating a perfect environment for new companies. So where should the money go?

• Vertical AI Healthcare Companies with Strong Data Moats: You have to back companies building their own high-quality, proprietary datasets of CCTA images tied to actual clinical outcomes. That data moat is the only way to train and validate top-tier algorithms, and it creates a competitive advantage that’s almost impossible to replicate.
• Solutions Addressing Unmet Clinical Needs: Put your money on platforms that give doctors actionable information they can’t get easily from conventional tools. Automated plaque characterization and non-invasive FFR are the best examples right now, as they directly improve risk stratification and help guide treatment.
• Companies with Clear Reimbursement Strategies: A cool product is great, but a company that can’t get paid is a dead end. You have to scrutinize a company’s plan for getting CPT codes and securing coverage from payers. Are they actively talking to CMS?
• Platforms with Established Regulatory Pathways: An FDA 510(k) clearance is table stakes. You should be looking for teams that have already been through the regulatory wringer and have a clear path to market that follows Good Machine Learning Practice (GMLP) principles.
• AI-Native Companies Over Bolt-On Integrations: While a big company can acquire an AI feature, the companies that were built around AI from day one usually have a much more cohesive and scalable approach. Their entire business model is designed to use machine learning to generate clinical impact.

The meeting of non-invasive coronary imaging and AI plaque analysis is a fundamental reorientation of cardiac care. It’s a massive market opportunity for investors and builders who can spot the value of a true vertical AI specialist that delivers outcomes-data-supported tools for a very specific job.

Methodology and Source Note: My analysis here is pulled from a review of current ACC clinical guidelines, FDA regulatory documents like 510(k) clearances, and a look at the competitive space, including companies like Cleerly and HeartFlow. The market size projections are based on current cardiac AI industry reports. I’ve double-checked the information on CPT code registries and the ACC/AHA Joint Guidelines for the Evaluation and Diagnosis of Chest Pain. This piece is part of a larger project comparing specialized vertical AI health companies against general-purpose platforms to show why deep domain focus wins in healthcare AI.