Cleerly's PARAMOUNT Trial Shows AI-QCT Enhances Cardiac Care Outcomes

On November 7, 2026, Cleerly announced the primary results of the PARAMOUNT trial, the first randomized study to evaluate AI-enabled quantitative coronary CT (AI-QCT) in guiding care for patients with suspected coronary artery disease (CAD). This trial, presented at the American Heart Association (AHA) Scientific Sessions, demonstrated that AI-QCT significantly improved diagnostic certainty and increased the initiation of lipid-lowering therapy compared to traditional care based on stress testing. The implications of these findings could reshape clinical practices in cardiology, particularly in how CAD is diagnosed and managed.

PARAMOUNT was a multicenter, open-label trial involving 530 symptomatic outpatients across 21 sites from January 2025 to February 2026. Participants, who had elevated LDL-C levels and no previous CAD history, were randomized into two groups: one receiving AI-QCT-guided care and the other undergoing usual care based on functional stress testing. The trial's results, derived from 523 participants with locked data, revealed that AI-QCT-guided care met its primary endpoint with a win ratio of 2.00, indicating a marked advantage over usual care. This finding is crucial as it suggests that AI-QCT can provide more accurate assessments of plaque and ischemia, leading to better patient outcomes.

The trial's results highlighted several key areas of improvement. Notably, diagnostic certainty regarding the cause of chest pain was a significant factor, with a 16.6 percentage point difference favoring AI-QCT. Furthermore, the use of lipid-lowering therapy surged from 31% to 75% in the AI-QCT group, compared to a modest increase from 32% to 47% in the usual care group. This increase in treatment initiation could have long-term implications for patient health outcomes, particularly in reducing cardiovascular events.

Moreover, the trial indicated a reduction in unnecessary invasive procedures. In the AI-QCT group, 38% of patients who underwent invasive coronary angiography did not require further intervention, compared to 60% in the usual care group. This suggests that AI-QCT not only enhances diagnostic accuracy but also optimizes the use of healthcare resources by minimizing unnecessary procedures.

The trial's demographic data is also noteworthy, with women constituting 57% of participants. This representation is significant in a field where women have historically been underrepresented in cardiovascular research. The inclusion of a diverse patient population may enhance the generalizability of the findings and encourage broader adoption of AI-QCT in clinical settings.

The results of PARAMOUNT signal a potential shift in the cardiology landscape. As healthcare systems increasingly adopt AI technologies, the success of AI-QCT could pave the way for more data-driven approaches in diagnosing and managing CAD. Competitors in the medical imaging and cardiology sectors may feel pressure to innovate and integrate similar AI solutions into their offerings.

Looking ahead, the anticipated publication of the full results in early 2027 will be critical for validating these findings and informing clinical guidelines. If confirmed, the integration of AI-QCT into routine care could not only improve patient outcomes but also reshape the economic dynamics of cardiovascular care, emphasizing preventive measures over invasive interventions. The advancements in AI-driven diagnostics may also catalyze further research and development in related fields, such as personalized medicine and remote patient monitoring.