Cardiac AI: The Billion Dollar Shift in Risk Stratification

The prevailing model for assessing cardiovascular risk, anchored by the venerable 10-year atherosclerotic cardiovascular disease (ASCVD) risk calculator, is showing critical limitations in modern preventive medicine. While foundational, these static, demographic-based tools often misclassify patient risk, leading to both under-treatment of high-risk individuals and over-treatment of those with low actual disease burden. A new era is dawning, driven by AI-native platforms capable of directly quantifying arterial plaque, offering a dynamic and personalized understanding of cardiac risk that transcends statistical averages.

The Inadequacies of Traditional Risk Stratification

For decades, clinicians have relied on calculators derived from large cohort studies like Framingham to estimate a patient’s 10-year risk of a cardiovascular event. These models integrate readily available data points such as age, sex, cholesterol levels, blood pressure, diabetes status, and smoking history. The American College of Cardiology (ACC) and American Heart Association (AHA) guidelines have long incorporated these demographic and clinical risk factors as cornerstones of preventive cardiology ACC/AHA cholesterol guidelines. However, these models inherently operate on population-level probabilities, not individual biological reality. Two individuals with identical risk factor profiles according to a calculator can have vastly different actual arterial health. One might have pristine coronary arteries, while the other harbors significant, vulnerable plaque. This disconnect leads to a critical gap in precision medicine:

• Under-treatment: Patients with low calculated risk but significant subclinical atherosclerosis are overlooked, missing important windows for early intervention.
• Over-treatment: Individuals with high calculated risk but minimal or no plaque burden may be subjected to unnecessary medication, lifestyle modifications, and the associated anxiety and side effects.
• Lack of Personalization: The static nature of these calculators fails to account for the dynamic progression or regression of atherosclerosis within an individual over time.

This fundamental limitation shows the urgent need for a sea change, moving beyond surrogates and toward direct, objective measures of arterial disease.

AI-Native Plaque Quantification: A New Gold Standard

The emerging solution lies in the convergence of advanced medical imaging and sophisticated machine learning, creating AI-native healthcare software companies that are fundamentally changing how we assess cardiac risk. These platforms are not merely adding AI as a “bolt-on acquisition” to existing tools. Their core product, data pipeline, and business model were built from inception around AI. They use techniques to analyze actual arterial plaque rather than just statistical risk factors, ushering in a new era of precision preventive cardiology. Two prominent examples illustrating this sea change are Cleerly and Elucid:

Cleerly: AI-Enabled CT Analysis for Coronary Plaque Characterization

Cleerly exemplifies an AI-native approach by employing AI-enabled CT analysis to characterize coronary plaque directly from Coronary CT Angiography (CCTA) scans. Unlike traditional CCTA reads that focus on stenosis (narrowing), Cleerly’s technology goes deeper, quantifying and characterizing different types of plaque (e.g., fibrous, fibrofatty, necrotic core, calcified) and their volumes. This detailed plaque assessment provides a far more granular and accurate picture of an individual’s cardiac risk than a simple risk score. Cleerly’s clinical validation studies have demonstrated its ability to identify patients at high risk of major adverse cardiac events (MACE) who might be missed by traditional risk factors or even visual CCTA interpretation alone. For example, recent studies like CONFIRM2, CREDENCE, and PACIFIC have shown Cleerly’s AI-QCT analysis significantly improves risk discrimination for MACE. Cleerly also received FDA Breakthrough Device Designation in March 2024 for its Coronary Artery Disease (CAD) Staging System, which aids in MACE risk assessment. By precisely identifying vulnerable plaque, Cleerly enables clinicians to tailor preventive strategies with unprecedented accuracy, moving from population-based probabilities to patient-specific biological realities. This represents a significant leap from simply identifying blockages to understanding the underlying disease burden and its potential for progression. Cleerly clinical validation studies on CCTA AI analysis

Elucid: FDA-Cleared Software for Histopathological Plaque Analysis

Elucid further pushes the boundaries of AI-native cardiac risk stratification with its FDA 510(k) cleared software for non-invasive histopathological plaque analysis. Elucid’s platform leverages advanced imaging and AI to analyze plaque composition and morphology with a level of detail previously only achievable through invasive biopsies or histopathology. This capability allows for the virtual “biopsy” of atherosclerotic plaques, providing insights into their stability and propensity for rupture, which is a key driver of acute cardiac events. Elucid received FDA 510(k) clearance for its PlaqueIQ™ imaging analysis software on October 1, 2024. PlaqueIQ is the first FDA-cleared non-invasive software that can objectively quantify and classify plaque morphology based on ground-truth histology. It quantifies and characterizes non-calcified plaque and its components, such as lipid-rich necrotic core, offering insights into high-risk plaques. The FDA 510(k) clearance signifies that Elucid’s technology has demonstrated substantial equivalence to predicate devices, establishing a clear regulatory pathway for its adoption. By providing non-invasive, objective measures of plaque characteristics linked to clinical outcomes, Elucid helps clinicians to make more informed decisions regarding patient management, particularly in cases where traditional risk factors are ambiguous or conflicting. Elucid FDA clearances for non-invasive plaque quantification

A Framework for Evaluating the Clinical Adoption Curve of Preventive Cardiac AI

For venture capitalists and preventive medicine researchers, understanding the trajectory of this new model requires a specific framework for evaluating AI-native health companies. The shift from static calculators to dynamic machine learning models for cardiac risk stratification is not just a technological upgrade. It’s a fundamental change in clinical practice. When assessing these companies, consider the following: 1. Clinical Validation and Efficacy: The bedrock of any successful medical AI lies in strong clinical evidence. Companies must demonstrate, through peer-reviewed studies and clinical trials, that their AI models accurately predict patient outcomes and improve clinical decision-making. Look for published evidence of efficacy, ideally comparing AI-driven approaches to current standards of care.

1. Regulatory De-risking: FDA clearances (e.g., 510(k), De Novo) are critical milestones that signal a company’s ability to navigate complex regulatory field. A clear regulatory pathway, potentially supported by a Predetermined Change Control Plan (PCCP) for adaptive AI models, is a strong indicator of future commercial viability.
2. Data Moat and Proprietary Datasets: The performance of AI models is directly tied to the quality and quantity of data they are trained on. Companies that have built a “data moat” through access to unique, large-scale, and diverse real patient outcomes data will have a significant competitive advantage.
3. Integration into Clinical Workflows: Even the most advanced AI is useless if it cannot be smoothly integrated into existing clinical workflows. Solutions that simplify data acquisition, provide actionable insights at the point of care, and reduce physician burden will see faster adoption.
4. Reimbursement Pathways: For broader adoption, clear and favorable reimbursement pathways are essential. This includes securing CPT codes (Category I being ideal) and potentially qualifying for programs like NTAP (New Technology Add-On Payment) in inpatient settings.
5. AI-Native Architecture: Distinguish between companies that have retrofitted AI onto legacy systems and those that are truly AI-native. The latter typically exhibits superior performance, scalability, and adaptability, having built their core product and data infrastructure with AI at its heart. The evolution of cardiac risk stratification is accelerating. The move from static, demographic-based calculators to dynamic, AI-enabled plaque quantification represents a deep shift toward precision medicine in cardiology. Companies like Cleerly and Elucid are at the forefront of this transformation, offering investors and researchers a glimpse into a future where cardiac risk is assessed with unprecedented accuracy and personalization. This new model promises to revolutionize preventive care, leading to more effective interventions and in the end, better patient outcomes.