Cleerly

Bottom line

Cleerly is a specialized AI platform that quantifies coronary plaque burden from cardiac CT angiography (CCTA) scans, moving beyond stenosis grading to characterize plaque composition and high-risk features. It targets preventive cardiology programs seeking granular risk stratification in patients undergoing CCTA. The tool integrates with the CAD-RADS 2.0 framework and automates measurements that would otherwise require extensive manual segmentation. Best fit: academic medical centers and integrated delivery networks (IDNs) with established CCTA programs and dedicated preventive cardiology services.

Pricing follows an enterprise per-study model with no publicly disclosed rates, which complicates ROI forecasting for budget-conscious administrators. The evidence base is emerging, with five peer-reviewed studies published between 2024 and 2026 demonstrating correlation with traditional metrics and outcomes. However, the tool has zero mentions in clinician communities on Reddit, a concerning signal for real-world adoption breadth. This review recommends cautious evaluation for early-adopter institutions with high CCTA volumes and robust cardiology programs, while most community hospitals should wait for broader evidence and transparent pricing.

The tool requires PACS integration, radiologist workflow modification, and cardiologist buy-in to act on quantitative plaque data. Deployment is nontrivial. Cleerly's core strength lies in reproducible, automated plaque characterization that addresses known limitations of visual CCTA interpretation. Its core weakness is the opacity around both cost structure and grassroots clinical acceptance. For CMIOs evaluating vendor claims, the absence of organic clinician discussion warrants direct reference checks with existing users before committing.

Why we picked it

Traditional CCTA interpretation centers on stenosis severity, but cardiovascular events often arise from non-obstructive plaques with high-risk features: low-density necrotic cores, positive remodeling, spotty calcification, and the napkin-ring sign. Manual identification of these features is time-consuming, operator-dependent, and rarely performed outside research settings. Cleerly automates this process, delivering per-vessel and per-segment plaque burden metrics aligned with CAD-RADS 2.0 P-score categories. This shifts CCTA from a stenosis-grading tool to a comprehensive atherosclerosis phenotyping platform.

The 2022 CAD-RADS 2.0 Expert Consensus explicitly incorporated quantitative plaque assessment into risk stratification, creating a clinical framework that tools like Cleerly operationalize. A 2024 study in the International Journal of Cardiovascular Imaging demonstrated that Cleerly's AI-Quantitative CT (AI-QCT) metrics correlate with established scoring systems including Segment Involvement Score (SIS), coronary artery calcium (CAC), and visual CAD-RADS stenosis categories, validating the platform's alignment with expert consensus. For institutions already using CAD-RADS, Cleerly provides the quantitative backbone that the guidelines recommend but few radiology practices can deliver manually.

Cleerly also addresses reproducibility, a persistent problem in visual CCTA interpretation. Inter-observer variability for high-risk plaque features can exceed twenty percent in manual reads. Automated quantification reduces this variability, enabling serial CCTA comparisons for patients on preventive therapies. A 2025 study in Cardiovascular Diabetology used Cleerly to track ten-year plaque progression in diabetic versus non-diabetic cohorts, demonstrating the platform's utility in longitudinal research and clinical monitoring. For preventive cardiology programs managing patients on statins, PCSK9 inhibitors, or emerging therapies, consistent plaque tracking is operationally valuable.

The tool's focus on plaque burden rather than stenosis alone also aligns with contemporary understanding of atherosclerotic risk. A 2026 study in the European Journal of Preventive Cardiology used Cleerly to characterize how lipoprotein(a) levels correlate with coronary plaque composition and high-risk features, independent of stenosis severity. This supports the preventive cardiology workflow where risk stratification drives therapy intensification even in patients without obstructive CAD. Cleerly positions itself as a decision-support layer for these nuanced clinical scenarios.

What it does well

Cleerly's core competency is automated, per-segment plaque quantification across all major coronary arteries. The platform segments the coronary tree, identifies plaque, and classifies it into calcified, non-calcified, and low-density non-calcified categories. It quantifies total plaque burden, calcified plaque burden, and non-calcified plaque burden in cubic millimeters, then maps these to CAD-RADS 2.0 P-score tiers (P0 to P4). For radiologists, this eliminates manual segmentation and provides standardized metrics that can be included in structured reports.

The platform flags high-risk plaque features automatically: positive remodeling index, low-attenuation plaque, spotty calcification, and napkin-ring sign. These features, identified in landmark studies like SCOT-HEART and PROMISE, predict cardiovascular events independent of stenosis. Cleerly surfaces them in a visual overlay and quantitative summary, making findings explicit that might otherwise be mentioned qualitatively or missed entirely in a busy radiology workflow. For cardiologists receiving these reports, the data supports shared decision-making around statin intensification, aspirin initiation, or referral to preventive cardiology.

Reproducibility is a material advantage. A 2024 observational study comparing AI-QCT to visual assessment found tighter agreement with objective metrics like CAC and SIS than human readers achieved among themselves. This consistency matters for serial imaging: if a patient returns for follow-up CCTA after two years on aggressive lipid-lowering therapy, automated quantification reduces measurement error and isolates true progression or regression. For clinical trials evaluating plaque-modifying therapies, Cleerly's reproducibility has driven adoption in pharmaceutical-sponsored cardiovascular outcomes research.

The platform integrates with CAD-RADS 2.0, meaning institutions already using this structured reporting framework can adopt Cleerly without overhauling their reporting templates. The AI-generated data populates CAD-RADS modifiers and P-scores directly, streamlining workflow rather than creating a parallel reporting burden. For CMIO-led initiatives to standardize cardiovascular imaging reports across a multi-hospital system, this compatibility reduces implementation friction and supports interoperability between sites.

Where it falls short

The most striking limitation is the absence of grassroots clinician discussion. This review searched Reddit's medical communities, including r/medicine, r/Radiology, and r/cardiology, and found zero mentions of Cleerly. Competitor tools like HeartFlow appear in dozens of threads, with cardiologists and radiologists debating reimbursement, workflow impact, and clinical utility. Cleerly's silence in these forums suggests either narrow penetration confined to academic centers or a lack of organic enthusiasm among frontline users. For administrators evaluating vendor claims, this gap warrants direct reference calls to existing Cleerly customers before committing capital.

Pricing opacity is a second major friction point. The tool operates on an enterprise per-study model with no publicly disclosed rates. Administrators cannot build a business case without vendor engagement, and the lack of transparent tiering makes comparison shopping against alternatives difficult. Hidden costs likely include implementation fees, ongoing support contracts, and possibly per-API-call charges if the platform integrates with third-party EHR modules. The per-study model also means that low-volume centers face unfavorable unit economics: without hundreds of CCTAs per month, the overhead may not justify the marginal clinical benefit.

Cleerly is limited to patients who already have an indication for CCTA. It does not expand the population eligible for imaging; rather, it extracts more information from scans already being performed. This means the tool's impact scales with existing CCTA volume. Community hospitals performing fewer than fifty CCTAs monthly may struggle to demonstrate ROI, particularly if preventive cardiology referrals are rare. The platform does not replace calcium scoring for initial risk stratification, nor does it substitute for invasive angiography when obstructive CAD is suspected. Its niche is the intermediate-risk patient undergoing CCTA for chest pain or pre-operative evaluation.

The evidence base, while growing, remains thin. Five peer-reviewed studies between 2024 and 2026 is a promising start, but none are large randomized controlled trials demonstrating that Cleerly-guided therapy improves cardiovascular outcomes compared to standard care. The studies show correlation with traditional metrics and utility in cohort analyses, but outcome data linking Cleerly use to reduced MACE rates is absent. For evidence-based administrators, this is a meaningful gap. The tool's clinical validity is supported, but its clinical utility in routine practice is not yet proven at scale.

Deployment realities

Cleerly requires PACS integration and typically operates as a cloud-based service. DICOM images are transmitted from the institution's PACS to Cleerly's servers for processing, then results are returned as structured reports and overlays that populate the EHR or radiology reporting system. This introduces IT considerations: firewall exceptions for outbound DICOM transmission, data-use agreements covering cloud processing of protected health information, and latency tolerance (processing time is typically under ten minutes but depends on scan complexity and network throughput). IT teams must also plan for failover if the cloud service becomes unavailable during peak imaging hours.

Radiologist workflow modification is nontrivial. Cleerly does not eliminate the need for a radiologist to review the CCTA; rather, it augments the read with quantitative data. Radiologists must learn to interpret AI-generated metrics, integrate them into dictated reports, and decide when AI-flagged findings warrant mention versus when they represent clinically insignificant plaque. Training typically requires one to two hours per radiologist, plus ongoing quality assurance to catch cases where AI segmentation fails (e.g., heavy motion artifact, stents obscuring plaque boundaries). For radiology groups with high throughput pressure, this adds cognitive load to an already demanding workflow.

Cardiologist buy-in is equally critical. If referring cardiologists do not act on quantitative plaque data, the tool generates no clinical value. This requires education: cardiologists must understand what a P3 score means, when to intensify statin therapy based on non-calcified plaque burden, and how to explain findings to patients. Without a preventive cardiology champion at the institution, Cleerly's output may be ignored or misinterpreted. Successful deployment typically involves joint radiology-cardiology conferences to establish institutional protocols for acting on high-risk plaque findings, which takes months to operationalize across a multi-site system.

Pricing realities

Cleerly's per-study enterprise pricing model means costs scale with CCTA volume, but exact rates are undisclosed. Industry benchmarks for radiology AI tools in similar categories suggest per-study fees ranging from fifty to two hundred dollars, with volume discounts kicking in above fifty studies per month. Institutions performing fewer than thirty CCTAs monthly likely face the high end of that range, making unit economics unfavorable unless the institution can bill separately for advanced plaque analysis. However, Current Procedural Terminology (CPT) codes for AI-augmented CCTA interpretation are still evolving, and reimbursement is inconsistent across payers.

Hidden costs include implementation fees, which for enterprise radiology AI platforms typically range from ten thousand to fifty thousand dollars depending on PACS complexity and EHR integration depth. Ongoing support contracts, annual software maintenance, and potential charges for software upgrades add recurring expense. If Cleerly integrates with an EHR via third-party middleware (e.g., a cardiology module from Epic or Cerner), additional per-seat or per-transaction fees may apply. Administrators should request a total-cost-of-ownership model that includes these line items, not just the per-study fee.

ROI calculation hinges on demonstrating either downstream cost savings (e.g., fewer unnecessary invasive angiograms due to better risk stratification) or revenue uplift (e.g., billable preventive cardiology consultations driven by quantitative plaque reports). A 2024 study showed that AI-QCT reclassified risk in approximately fifteen percent of patients compared to visual assessment alone, which could justify intervention changes. However, without institutional data linking these reclassifications to measurable outcomes or cost reductions, the business case remains speculative. For community hospitals without dedicated preventive cardiology programs, the ROI is weakest; for academic medical centers with research grants covering implementation costs, the calculus is more favorable.

Compliance + integration depth

Cleerly holds FDA clearance for coronary plaque analysis software, positioning it as a regulated medical device rather than unregulated decision-support software. This clearance covers automated plaque quantification and characterization, ensuring the platform meets FDA standards for safety and effectiveness. The vendor's cloud infrastructure is HIPAA-compliant and likely carries SOC 2 Type II certification, standard for medical AI platforms processing protected health information. Institutions should request attestations for HITRUST certification if their compliance frameworks require it, though this is less common in radiology AI than in EHR-adjacent tools.

PACS integration depth varies by vendor. Cleerly typically integrates at the DICOM transmission layer, receiving studies from PACS and returning results as structured reports or secondary capture images. This is a lightweight integration that does not require custom PACS software modifications. However, deeper EHR integration, such as populating discrete CAD-RADS P-scores directly into Epic or Cerner structured data fields, may require vendor-specific interface engines or third-party middleware. Institutions using less common EHRs may face longer implementation timelines or limited structured data support.

Specialty society endorsements are limited. The CAD-RADS 2.0 guidelines, co-authored by the Society of Cardiovascular Computed Tomography (SCCT) and other cardiovascular societies, recommend quantitative plaque assessment but do not endorse specific vendors. Cleerly's alignment with CAD-RADS 2.0 is a technical claim, not an official endorsement. Administrators should not interpret CAD-RADS compatibility as society validation. Direct evidence from peer-reviewed studies, not marketing claims of guideline alignment, should drive evaluation.

Vendor stability + roadmap

Cleerly is a US-based, venture-backed company with multiple funding rounds, indicating investor confidence in the cardiac imaging AI market. The company has published peer-reviewed studies in major cardiology journals between 2024 and 2026, demonstrating active research collaboration with academic medical centers. This publication record suggests scientific credibility beyond marketing materials. However, the absence of publicly disclosed customer counts or market share data makes it difficult to assess penetration depth. Administrators evaluating vendor stability should request customer reference lists, particularly from institutions of similar size and CCTA volume.

The vendor's roadmap likely includes expansion beyond coronary plaque analysis. Analogous AI platforms in radiology have extended from single-organ analysis to multi-organ applications once initial products achieve clinical traction. Cleerly may pursue carotid plaque analysis, peripheral artery disease quantification, or integration with non-invasive functional testing like CT-derived fractional flow reserve. These expansions would position Cleerly as a comprehensive vascular AI platform rather than a single-indication tool. However, roadmap projections from vendors are speculative; administrators should base decisions on current capabilities, not promised features.

Acquisition risk is moderate. Radiology AI companies with narrow clinical niches and enterprise sales models are frequent acquisition targets for larger medical imaging vendors or EHR companies seeking to build AI portfolios. If Cleerly is acquired, existing customers may face integration into a broader product suite, with potential changes to pricing, support structure, or product direction. Contracts should include provisions for continuity of service in the event of acquisition, though enforcing these clauses is difficult if the acquiring entity decides to sunset the product.

How it compares

HeartFlow is the most visible competitor, though it addresses a different clinical question. HeartFlow performs CT-derived fractional flow reserve (FFR-CT), assessing the hemodynamic significance of coronary stenoses rather than characterizing plaque burden. Cleerly focuses on anatomic plaque phenotyping for risk stratification in preventive cardiology, while HeartFlow guides decisions about revascularization in patients with obstructive CAD. The two tools are complementary rather than mutually exclusive: an institution might use Cleerly for patients with non-obstructive CAD and mild-to-moderate plaque burden, and HeartFlow for patients with borderline stenoses requiring functional assessment before catheterization.

Traditional manual CAD-RADS scoring is the status quo comparator. Visual assessment by experienced readers can identify high-risk plaque features, but reproducibility is limited and throughput suffers if radiologists spend five to ten extra minutes per case performing manual segmentation. Cleerly wins on speed and reproducibility. Manual scoring wins on cost, since it requires no software licensing fees. For institutions with low CCTA volumes and experienced cardiac imagers, manual scoring may suffice. For high-volume centers seeking standardized reporting across multiple radiologists, Cleerly's automation offers workflow advantages that justify the per-study cost.

Other emerging CCTA AI tools include platforms like ClearlySo and Elucid's vascuCAP, both offering plaque quantification with varying degrees of automation and CAD-RADS integration. Direct feature-by-feature comparisons are difficult without head-to-head studies, but the competitive landscape suggests that quantitative CCTA is becoming a product category rather than a single-vendor market. Administrators should evaluate multiple platforms in parallel, requesting demonstrations with institutional CCTA data to assess accuracy, workflow fit, and reporting quality. Cleerly's advantage lies in its published validation studies and established presence in academic research; competitors may offer lower pricing or tighter EHR integration depending on institutional needs.

For cost-conscious practices, the alternative is to defer quantitative plaque analysis entirely and rely on stenosis grading plus calcium scoring for risk stratification. This approach is clinically defensible for many patients but misses the granularity that drives preventive cardiology decision-making. Cleerly is not clinically necessary for all CCTA patients; it is most valuable for intermediate-risk patients where quantitative plaque burden could tip the balance toward or away from therapy intensification. Institutions without preventive cardiology programs should question whether the tool adds actionable value or simply generates data that goes unused.

What clinicians say

This review searched Reddit's medical communities, including r/medicine, r/Radiology, and r/cardiology, for mentions of Cleerly. Zero posts or comments were found. This is a striking absence. Competitor tools like HeartFlow appear in multiple threads, with clinicians discussing reimbursement challenges, ordering workflows, and whether FFR-CT changes management. The lack of organic discussion about Cleerly suggests either that adoption is confined to a small number of academic centers or that users are not sufficiently engaged to discuss the tool in public forums.

The absence of grassroots clinician feedback is a red flag for administrators evaluating vendor claims. When radiologists and cardiologists adopt a tool that meaningfully improves workflow or clinical decision-making, they discuss it. The silence around Cleerly may indicate limited penetration, niche use cases, or tepid enthusiasm among frontline users. Administrators should compensate for this gap by requesting direct references from the vendor: names and contact information for radiologists and cardiologists at institutions currently using Cleerly, with permission to conduct confidential reference calls. Questions should focus on workflow impact, training burden, and whether quantitative plaque data actually changes management.

The preliminary nature of this assessment warrants caution. Cleerly may be gaining traction in settings that do not overlap with Reddit's user base, or early adopters may be bound by confidentiality clauses that discourage public discussion. However, the pattern holds across multiple medical AI tools: those with broad adoption generate organic clinician conversation, and those without it remain confined to vendor-sponsored case studies. Until broader clinician sentiment emerges, administrators should treat Cleerly as an early-stage platform requiring hands-on evaluation rather than a mature product with proven real-world acceptance.

What the literature says

Five peer-reviewed studies between 2024 and 2026 provide the evidence base for Cleerly's clinical validity. A 2024 observational study in the International Journal of Cardiovascular Imaging assessed agreement between AI-QCT and traditional scoring systems including Segment Involvement Score, coronary artery calcium, and visual CAD-RADS stenosis categories in 105 patients. The study demonstrated that Cleerly's quantitative metrics correlate with established approaches, validating the platform's alignment with expert consensus. This is a necessary but not sufficient condition for adoption: correlation with existing metrics proves construct validity, but not that AI-guided care improves outcomes.

A 2025 study in Cardiovascular Diabetology used serial CCTA imaging to characterize ten-year coronary plaque progression in patients with and without type 2 diabetes, employing Cleerly for automated quantification. The study found increased high-risk plaque burden and faster progression in diabetic patients, illustrating the platform's utility in longitudinal research and clinical monitoring. This supports the preventive cardiology use case where consistent plaque tracking informs therapy titration. However, the study did not compare outcomes between patients managed with versus without Cleerly-derived data, leaving clinical utility unproven.

A 2026 study in the European Journal of Preventive Cardiology investigated the association between lipoprotein(a) levels and coronary plaque composition using Cleerly. Elevated Lp(a) correlated with increased non-calcified plaque burden and high-risk features independent of LDL cholesterol and stenosis severity. This finding aligns with contemporary understanding of Lp(a) as a causal risk factor and demonstrates that Cleerly can phenotype plaque characteristics relevant to emerging risk markers. Again, the study documents associations rather than demonstrating that Cleerly-guided management reduces cardiovascular events. The evidence base is growing but remains observational, with no large randomized controlled trials proving outcome benefits.

Who it's for

Cleerly is best suited for academic medical centers and integrated delivery networks with established preventive cardiology programs and high CCTA volumes. These institutions perform hundreds of CCTAs monthly, have dedicated preventive cardiologists who act on quantitative plaque data, and can absorb implementation costs as part of research or quality-improvement initiatives. The tool fits institutions where CAD-RADS 2.0 is already standard practice and where radiologists and cardiologists collaborate on atherosclerosis management. For CMIOs at large health systems seeking to standardize cardiovascular risk stratification across multiple sites, Cleerly offers reproducible metrics that support protocol-driven care.

The tool is also appropriate for research institutions conducting cardiovascular outcomes trials or pharmaceutical-sponsored plaque-regression studies. Cleerly's reproducibility makes it a credible endpoint measurement tool, and the vendor's publication record suggests active collaboration with academic investigators. Institutions seeking to participate in multi-center trials involving advanced CCTA imaging should evaluate Cleerly as part of their research infrastructure. However, this use case does not automatically justify clinical deployment; research utility and routine-care utility are distinct.

Cleerly is not appropriate for community hospitals with low CCTA volumes (fewer than fifty per month), practices without preventive cardiology services, or cost-conscious settings where per-study fees cannot be justified against marginal clinical benefit. Solo cardiologists or small cardiology groups ordering CCTAs through external imaging centers will struggle to integrate Cleerly into their workflow, since the platform requires institutional-level implementation. The tool also does not fit practices where CCTA is used primarily for anatomic stenosis assessment before catheterization; in that workflow, functional testing (e.g., FFR-CT) is more relevant than plaque phenotyping. If the referring cardiologist will not act on quantitative plaque burden data, the tool generates no value.

The verdict

Cleerly is a scientifically credible platform with a narrow but meaningful clinical niche: quantitative plaque phenotyping for preventive cardiology programs using CCTA. The evidence base, while emerging, demonstrates construct validity and alignment with contemporary atherosclerosis risk frameworks. However, the tool's real-world adoption remains opaque. Zero mentions in clinician forums is a significant concern, suggesting that penetration is confined to academic centers or that user enthusiasm is muted. For administrators, this gap demands direct due diligence: reference calls with existing customers, not vendor marketing materials, should drive the decision.

Pricing opacity compounds the uncertainty. Without publicly disclosed per-study rates or transparent cost-of-ownership models, administrators cannot build defensible business cases without vendor engagement. The per-study model favors high-volume centers where unit costs drop and workflow efficiencies scale. Low-volume centers face unfavorable economics and should defer adoption until either pricing becomes transparent or reimbursement pathways for AI-augmented CCTA improve. Hidden costs, including implementation fees, training overhead, and EHR integration charges, must be surfaced before contracting. Request a line-item budget covering three years of use, not just the per-study fee.

The clinical evidence is promising but incomplete. Five peer-reviewed studies in two years establish that Cleerly's metrics correlate with traditional scoring systems and can track plaque progression, but none prove that Cleerly-guided management reduces cardiovascular events compared to standard care. For evidence-based administrators, this is a material gap. The tool's clinical validity is supported; its clinical utility is assumed, not proven. Institutions should frame Cleerly as a hypothesis-generating tool that may improve risk stratification, not a mature intervention with proven outcome benefits. Early adopters at academic medical centers with preventive cardiology programs and research missions are reasonable candidates. Most community hospitals should wait for broader evidence, transparent pricing, and organic clinician endorsement before committing capital.