In the global battle against cardiovascular disease—the leading cause of mortality worldwide—a significant technological milestone has been reached. Cleerly, a Colorado-based health technology firm, has unveiled groundbreaking data from its PARAMOUNT trial, signaling a potential shift in how physicians diagnose and manage coronary artery disease (CAD). The study, presented as Late-Breaking Science at the 2026 American Heart Association (AHA) Scientific Sessions in Chicago, suggests that AI-powered quantitative coronary CT analysis (AI-QCT) significantly outperforms the traditional standard of care (SOC) in precision, clinical decision-making, and patient outcomes.
As CAD continues to affect approximately 244 million individuals globally, according to the US Centers for Disease Control and Prevention (CDC), the findings from the PARAMOUNT trial arrive at a critical juncture in medical practice, promising to refine the way clinicians approach the millions of patients presenting with stable chest pain.
The Core Findings: A New Standard of Diagnostic Certainty
At the heart of the PARAMOUNT trial (NCT06713239) is a comparison between traditional stress testing—the current cornerstone of AHA/ACC clinical guidelines—and a novel, AI-driven approach to imaging. Cleerly’s AI-QCT software, which utilizes advanced algorithms to analyze coronary atherosclerotic plaque, was tested against the "usual care" strategy to determine which approach provides better clarity for clinicians and better results for patients.
The trial’s primary endpoint was a prespecified hierarchical composite measure at 180 days. The results were decisive: plaque-based care was favored over the standard of care with a win ratio of 2.00. In unmatched patient comparisons, those managed via the plaque-based AI approach achieved superior clinical results 46.7% of the time, compared to just 23.4% in the usual care group.
The study’s most significant contributor to this success was an improvement in diagnostic certainty. Clinicians utilizing the AI-QCT platform were 16.6 percentage points more certain in their assessment of whether a patient’s chest pain was definitively caused by CAD compared to their counterparts relying on stress testing. This reduction in diagnostic ambiguity is being hailed as a major win for both cardiologists and patients, as it minimizes the "diagnostic odyssey" that often leaves symptomatic individuals without a clear path forward.
Chronology of the PARAMOUNT Investigation
The trajectory of the PARAMOUNT trial reflects the increasing integration of machine learning into clinical workflows.
- Study Design and Enrollment: The study enrolled 523 symptomatic patients, with a commendable focus on diversity. Notably, 300 of the participants were women—a demographic historically underrepresented in cardiovascular research.
- Methodology: Patients were randomized to receive either the traditional stress-testing pathway or the experimental AI-QCT assessment. The AI arm utilized Cleerly’s Labs and ISCHEMIA software to visualize and quantify the burden of atherosclerotic plaques—deposits of fat, cholesterol, and calcium that obstruct arterial flow.
- The 180-Day Benchmark: The trial tracked outcomes over a six-month period, focusing on changes in medical management, the necessity of invasive procedures, and the initiation of preventative therapies.
- AHA Presentation (October 2026): The preliminary data was shared with the global medical community during the AHA Scientific Sessions in Chicago (October 7-9, 2026), sparking intense discussion regarding the future of diagnostic guidelines.
- Future Outlook (2027): While preliminary findings are already influencing clinical discourse, the full, comprehensive dataset is expected to be published in early 2027, which will likely serve as the primary catalyst for potential updates to international cardiology guidelines.
Supporting Data: Translating Insights into Action
The quantitative data emerging from PARAMOUNT offers a compelling narrative regarding the "actionability" of AI-led diagnostics. Beyond merely identifying the presence of disease, the AI-QCT approach fundamentally changed the therapeutic trajectory for the participants.
Lipid-Lowering Therapy and Preventive Care
Perhaps the most striking outcome involves the adoption of preventative medical therapy. In the AI-QCT group, the utilization of lipid-lowering therapy skyrocketed from 31% at baseline to 75% post-testing. Conversely, the usual care arm saw a much more modest increase, rising from 32% to only 47%. This suggests that when patients and clinicians are presented with visual, data-backed evidence of their plaque burden, adherence to preventative medicine becomes significantly more likely.
Efficiency in Invasive Procedures
The trial also shed light on the inefficiencies of current invasive practices. Among patients who underwent invasive coronary angiography, 38% in the AI-QCT group did not go on to receive revascularization or bypass surgery. In the usual care arm, that number was 60%. This indicates that the AI-QCT approach is better at identifying patients who do not actually require invasive intervention, thereby sparing them the risks, costs, and recovery times associated with unnecessary procedures.
Official Responses: The Voice of Clinical Leadership
The medical community has reacted to these findings with a mix of optimism and scientific rigor. Udo Hoffman, MD, Cleerly’s chief medical, scientific, and AI officer, emphasized that this is the first time such technology has been proven to support clinicians in the full spectrum of CAD management.
"PARAMOUNT’s preliminary results demonstrate how AI-QCT supports clinicians in diagnosing, staging, and treating CAD," Hoffman stated. "It unlocks the potential benefits of both medical management and interventional treatment for patients with stable chest pain, providing a roadmap that was previously obscured by the limitations of traditional stress testing."
Todd Villines, co-principal investigator of the PARAMOUNT trial and Donald Barnes Professor of Cardiology at the University of Virginia, echoed these sentiments, highlighting the trial’s commitment to representative research. "We’ve evaluated patients with chest pain by looking for blockages or reduced blood flow for decades," Villines noted. "PARAMOUNT shows that when we measure plaque directly and use it to guide care, physicians gain clearer answers, and patients are far more likely to start preventive therapy. We’re also proud that more than half of our participants were women, a group too often underrepresented in cardiovascular trials."
Implications: A New Era for Cardiovascular Health
The implications of the PARAMOUNT trial extend far beyond the walls of the research lab. As the healthcare industry grapples with rising costs and the need for better patient outcomes, the shift toward "plaque-based care" represents a move toward personalized, precision medicine.
1. Shift from "Functional" to "Structural" Diagnosis
For years, the standard of care has focused on functional testing—essentially asking, "Is there a restriction in blood flow?" The PARAMOUNT results suggest that we should instead be asking, "What is the structural reality of the artery?" By visualizing the atherosclerotic plaque itself, clinicians can move from reactive treatment to proactive, preventative management.
2. Economic and Resource Allocation
By reducing the number of unnecessary invasive angiograms, the healthcare system could potentially save millions of dollars in hospital expenditures. Furthermore, the increased efficacy of preventative therapy—as seen in the jump in lipid-lowering medication usage—could lead to a long-term reduction in cardiovascular events such as heart attacks and strokes, further alleviating the burden on healthcare infrastructure.
3. The Democratization of Expertise
AI-QCT acts as a force multiplier for cardiologists. By providing standardized, quantitative data on plaque volume and characteristics, the technology reduces inter-observer variability, ensuring that a patient in a rural clinic receives the same high-level diagnostic interpretation as a patient at a major academic research center.
4. Future Guideline Revisions
With the full trial results slated for early 2027, the medical community is already bracing for potential revisions to AHA/ACC guidelines. If the data holds, the pressure on health systems to adopt AI-QCT will grow, potentially making it the new gold standard for symptomatic CAD patients.
Conclusion
The PARAMOUNT trial has provided a clear, evidence-based argument for the integration of artificial intelligence into daily cardiology practice. By transforming how we visualize, quantify, and treat coronary artery disease, Cleerly has moved the needle on one of the most persistent challenges in modern medicine. As we await the full publication of the study in 2027, the message from Chicago is clear: the future of cardiovascular care is precise, preventative, and increasingly reliant on the synergy between human expertise and machine intelligence.
For the 244 million people living with CAD, this transition represents more than just a technological upgrade—it represents a pathway to longer, healthier, and more certain lives.
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