DeepHealth

Bottom line

DeepHealth is the world's largest radiology AI portfolio following RadNet's March 2026 acquisition of Gleamer, combining modules for chest X-ray, mammography, CT, and MRI across cardiovascular, oncology, and neuro applications. It is not the most validated. With only one published study (a 2022 protocol paper for breast screening, no outcomes yet) and zero mentions in clinician communities, this is a breadth play with a thin evidence base.

The tool's strength is consolidation: one vendor relationship for multi-modality AI, backed by RadNet's distribution network and projected $140 million annual recurring revenue by end of 2026. Its weakness is lack of independent validation. Pricing is enterprise-only, quoted per site or per study, with no transparent tiers for smaller practices.

Best fit: large health systems or RadNet-affiliated imaging centers prioritizing administrative simplicity and willing to pilot unproven modules. Poor fit: evidence-driven CMIOs, smaller groups needing published ROI data, or specialty departments wanting best-in-class performance in one modality rather than a multi-tool bundle.

Why we picked it

This tool earned the AI Radiology silo pick for best global portfolio based on scale, not validation. The March 2026 Gleamer acquisition created the broadest single-vendor radiology AI offering, spanning more modalities and clinical indications than Aidoc, Lunit, or Annalise.ai. For a CMIO managing vendor sprawl across radiology, cardiology, and oncology, consolidating to one contract has genuine operational appeal.

RadNet's ownership matters. As one of the largest outpatient imaging operators in the United States, RadNet controls distribution: the company can deploy DeepHealth across its own network and use internal study volumes to refine algorithms without relying on external adopters. This vertical integration accelerates iteration cycles but raises conflict-of-interest questions when the vendor reads its own studies using its own AI.

The portfolio combines Gleamer's European regulatory track record with legacy iCAD ProFound AI technology for breast imaging, creating a hybrid offering with FDA clearances in some modules and CE marks in others. However, the acquisition is four months old as of May 2026. Integration timelines, combined workflows, and cross-modality performance data are not yet public.

We picked this tool because breadth is a legitimate decision criterion for large systems. We did not pick it because it is proven. Health systems evaluating DeepHealth should require pilot agreements with performance benchmarks before committing to enterprise contracts.

What it does well

DeepHealth excels at portfolio breadth. The combined offering spans chest X-ray nodule detection, mammography calcification and mass flagging, cardiac CT calcium scoring, brain MRI hemorrhage detection, and musculoskeletal fracture identification. Competitors typically specialize in one or two modalities. Aidoc covers multi-modality triage but focuses on critical findings (stroke, PE, intracranial hemorrhage). Lunit concentrates on chest and breast oncology. DeepHealth attempts to be the single AI layer across an entire radiology department.

The vendor stability is strong. RadNet is publicly traded on NASDAQ under ticker RDNT, reported over 8 million imaging studies annually as of 2025, and has acquisition capital to consolidate further. The projected $140 million ARR by end of 2026 signals market traction, even if independent validation lags. For procurement teams, vendor continuity risk is low compared to venture-backed startups.

Integration within the RadNet network is a documented advantage. Internal deployments allow RadNet to test modules on high study volumes before external release, iterate based on radiologist feedback from its own employed readers, and bundle AI into existing service contracts for affiliated hospitals. This creates a faster feedback loop than vendors reliant on multi-site external pilots.

The tool inherits Gleamer's European validation pedigree. Gleamer modules held CE marks and were deployed in French university hospitals before acquisition. While US FDA clearances for the merged portfolio are not fully detailed in public sources, the iCAD ProFound AI component has FDA 510(k) clearance for breast density assessment and lesion detection. Regulatory pathways are established, even if combined-module clearances post-acquisition are pending.

Where it falls short

The evidence base is alarmingly thin for a tool of this scale. One published study appears in PubMed: a 2022 BMJ Open protocol paper describing a planned cohort study of AI-enhanced breast cancer screening. This is a methods paper, not an outcomes report. No results are published. No multi-site validation studies for the chest, cardiac, or neuro modules appear in peer-reviewed literature as of May 2026. For a portfolio claiming to span cardiovascular, oncology, and neurology applications, the absence of published sensitivity, specificity, or ROI data is a red flag.

Clinician sentiment is invisible. Zero mentions of DeepHealth, Gleamer, or RadNet AI appear in radiology communities on Reddit as of the search date. This suggests the tool is marketed business-to-business to health system executives, not adopted grassroots by radiologists who then advocate for purchase. Lack of organic clinician discussion raises questions about user satisfaction and workflow fit. Tools radiologists love generate forum posts. Silence may indicate limited deployment outside RadNet's own network or radiologist indifference.

Vendor-owned AI creates conflict-of-interest concerns. RadNet reads millions of studies annually using its own algorithms, reports findings, and bills payers. While this model is not unique (hospital-owned AI is common), it removes the independent validation layer that exists when a third-party radiology group evaluates a vendor's claims before purchase. External health systems adopting DeepHealth cannot point to published outcomes from peer institutions. They are relying on RadNet's internal metrics, which are not independently audited.

Pricing opacity is standard for enterprise software but frustrating for smaller buyers. The listed pricing tier shows zero dollars with a note that contracts are per-site or per-study, negotiated case-by-case. No transparent volume tiers exist. A 200-bed community hospital cannot estimate costs without entering a sales cycle. Hidden costs likely include per-modality implementation fees, radiologist training across multiple AI modules, ongoing support contracts, and integration with non-RadNet PACS vendors. Per-API-call pricing models, common in AI tools, are not ruled out.

Deployment realities

Integration friction is modality-dependent. Within RadNet-affiliated centers using RadNet's preferred PACS and RIS vendors, deployment is likely streamlined. External health systems face standard DICOM interoperability challenges: ensuring AI outputs write back to the worklist, managing hanging protocols that surface AI findings, and training radiologists to interpret AI flags without over-relying on algorithmic suggestions. Multi-modality deployments multiply this complexity. A radiology department adopting DeepHealth for chest, breast, and cardiac imaging must train technologists and radiologists on three separate AI workflows.

Change management spans multiple specialties. Chest modules affect general radiologists and pulmonologists. Cardiac CT modules require buy-in from cardiologists and cardiac imagers. Neuro modules involve neurology and neurosurgery. A CMIO cannot deploy this tool with radiology IT alone. Cross-departmental governance is required, particularly if the AI auto-populates reports or changes referral triage workflows. Resistance from specialty physicians who prefer point solutions with stronger validation (HeartFlow for cardiac CT, Viz.ai for stroke) is predictable.

Training time per clinician is underspecified in public materials. Vendors typically quote 30 to 60 minutes per module for radiologist onboarding, but multi-modality portfolios require training across modules. A general radiologist covering chest, musculoskeletal, and neuro cases may need three hours of structured training plus supervised reading time. Smaller practices with limited CME budgets may struggle to allocate this time without backlog impact.

Pricing realities

Enterprise per-site or per-study pricing makes cost estimation impossible without vendor engagement. Public sources do not disclose representative per-study fees, annual site licenses, or volume tier breakpoints. Comparable radiology AI tools quote $1 to $3 per study for single-modality modules (Aidoc's head CT triage, for example). Multi-modality bundles may offer volume discounts, but DeepHealth's pricing model is opaque. A 500-bed IDN imaging 200,000 studies annually across covered modalities could face six-figure annual costs, but confirming this requires RFP participation.

Hidden costs include implementation, training, and support. PACS integration typically incurs one-time fees of $20,000 to $50,000 per site for HL7 interface work, DICOM routing configuration, and IT validation. Multi-site health systems deploying across five hospitals could spend $100,000 to $250,000 on integration alone before processing the first study. Ongoing support contracts, priced as a percentage of the annual license fee, add 15 to 20 percent annually. Vendor-led radiologist training, if required on-site, bills at consulting rates.

ROI math depends on study volume, modality mix, and workflow efficiency gains. Published ROI studies for radiology AI typically claim 10 to 20 percent radiologist time savings by prioritizing critical findings and pre-populating measurements. If a radiologist interpreting 40 chest CTs daily saves three minutes per study via AI-assisted nodule flagging, that is two hours daily, potentially allowing higher study throughput or reduced evening reading. However, without published outcomes specific to DeepHealth modules, buyers cannot validate these assumptions. Contract terms should include performance guarantees: minimum sensitivity thresholds, maximum false-positive rates, and time-to-triage benchmarks.

Compliance + integration depth

HIPAA compliance is baseline for a RadNet-owned product. RadNet operates under BAA agreements with hospital clients and payer contracts nationwide. SOC 2 Type II attestation is standard for healthcare SaaS vendors, but public sources do not confirm whether DeepHealth holds this certification independently or inherits RadNet's enterprise controls. HITRUST certification, preferred by risk-averse health systems, is not mentioned. Buyers should request attestation documents during procurement.

FDA clearance status is mixed. The legacy iCAD ProFound AI component for mammography holds FDA 510(k) clearance for breast density assessment and lesion detection. Gleamer modules held CE marks in Europe pre-acquisition, but US FDA clearances for the combined DeepHealth portfolio post-March 2026 are not enumerated in public materials. CMIOs should require a clearance matrix: which modules are FDA-cleared as CADe (computer-aided detection) versus CADx (computer-aided diagnosis), and which remain investigational or off-label.

EHR and PACS integration depth is unspecified. The tool must interoperate with Epic Radiant, Cerner (now Oracle Health) PowerChart, MEDITECH Radiology, and major PACS vendors (Sectra, GE Centricity, Philips IntelliSpace). Whether integration is read-only (AI flags visible in a side panel) or bi-directional (AI writes structured findings into discrete report fields) affects radiologist workflow. Bi-directional write capability reduces copy-paste errors but requires HL7 or FHIR interfaces that not all PACS vendors support. RadNet's own tech stack likely uses preferred vendors, so external adopters should pilot integration on their specific EHR and PACS before signing enterprise contracts.

Vendor stability + roadmap

RadNet is financially stable. As a publicly traded outpatient imaging operator with over $1 billion annual revenue (2025 figures), the company has acquisition capital and market presence. The March 2026 Gleamer acquisition, reportedly valued in the tens of millions, signals commitment to AI as a strategic pillar. The prior iCAD ProFound AI acquisition consolidated mammography AI. Further acquisitions in specialized modalities (cardiac, neuro) are plausible.

Customer references are not publicly named outside the RadNet network. Vendor case studies typically cite three to five external health systems as lighthouse customers. DeepHealth's public materials do not list non-RadNet adopters, suggesting the tool is still primarily an internal offering. External CMIOs should request referenceable sites during evaluation, ideally institutions of similar size and EHR vendor.

The roadmap is likely focused on cross-modality integration and RadNet network expansion. Combining Gleamer's chest and musculoskeletal modules with iCAD's breast imaging and adding cardiac and neuro creates a portfolio, but true differentiation would come from cross-modality insights: an AI that flags a lung nodule on chest CT and cross-references prior mammograms for metastasis risk, for example. Whether RadNet is building this level of integration or simply bundling separate modules is unclear. Public statements emphasize scale and coverage, not algorithmic innovation.

How it compares

Aidoc wins on published validation and triage focus. Aidoc's multi-modality platform prioritizes critical findings (intracranial hemorrhage, pulmonary embolism, cervical spine fracture) with FDA clearances and peer-reviewed sensitivity data. If a hospital's goal is reducing time-to-treatment for life-threatening findings, Aidoc's evidence base is stronger. DeepHealth's portfolio is broader, covering screening and non-urgent findings, but lacks head-to-head comparative studies.

Annalise.ai wins on comprehensive chest X-ray analysis. Annalise Enterprise analyzes over 120 chest X-ray findings simultaneously, with published validation in Radiology and deployment in Australian public hospitals. For centers prioritizing chest imaging, Annalise offers more granular detection than DeepHealth's chest module, with transparent per-study pricing. DeepHealth wins if the buyer needs chest plus breast plus cardiac in one contract.

Lunit wins on oncology-specific validation. Lunit INSIGHT CXR and MMG have published AUC data for lung nodule and breast lesion detection, regulatory clearances in the US, EU, and Asia, and adoption in academic cancer centers. If a breast imaging center wants proven mammography AI, Lunit or the standalone iCAD ProFound AI (now part of DeepHealth) are safer bets. DeepHealth's advantage is bundling oncology with cardiovascular and neuro, but per-modality performance may not match specialists.

Specialized point solutions (HeartFlow for cardiac CT, Viz.ai for stroke triage) win on depth and outcomes. HeartFlow FFR-CT has Medicare reimbursement codes and cardiologist adoption driven by published trial data. Viz.ai's stroke triage has real-world time-to-treatment studies. DeepHealth cannot match this level of specialty validation in any single modality. The comparison is breadth versus depth: one vendor relationship with unproven modules, or best-of-breed per specialty with vendor management overhead.

What clinicians say

Clinicians in public forums say nothing. Zero mentions of DeepHealth, Gleamer AI, or RadNet AI modules appear in radiology subreddits or physician discussion boards as of May 2026. This is unusual for a tool claiming the largest radiology AI portfolio globally. Comparable tools (Aidoc, Annalise, Lunit) generate regular posts from radiologists sharing workflow tips, false-positive frustrations, or adoption wins.

The silence suggests business-to-business sales to health system executives rather than grassroots radiologist advocacy. Tools that radiologists champion generate organic discussion. Tools purchased by CFOs to reduce costs or consolidate vendors may deploy without radiologist enthusiasm. External buyers should request live demonstrations with working radiologists, not just IT demonstrations, to assess whether the tool integrates into reading workflows or becomes shelfware.

Absence of evidence is not evidence of absence, but it is a yellow flag. Buyers should ask vendor sales teams for referenceable radiologists at non-RadNet sites willing to discuss workflow impact, false-positive rates, and training adequacy. If the vendor cannot provide peer contacts, that signals limited external deployment.

What the literature says

One study appears in PubMed: a 2022 BMJ Open protocol paper titled 'Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection.' This describes a planned study design for evaluating AI-enhanced mammography screening in a population cohort. It is a methods paper. No outcomes, sensitivity, specificity, or interval cancer detection rates are published. The study may still be enrolling or analyzing data as of May 2026.

Zero published outcomes for chest, cardiac, or neuro modules exist in peer-reviewed journals indexed in PubMed. For a portfolio claiming to span cardiovascular, oncology, and neurology applications, this is a critical gap. Competitors have published validation: Aidoc has studies in Radiology and Academic Emergency Medicine, Annalise has Radiology and Lancet Digital Health publications, Lunit has European Radiology and JAMA Network Open papers. DeepHealth's evidence gap means buyers are piloting unvalidated modules.

The BMJ Open protocol paper references AI's potential to reduce false negatives (interval cancers) without increasing false positives (overdiagnosis). This is the correct outcome to measure for screening tools. However, the paper's publication in 2022 predates the March 2026 Gleamer acquisition, so it likely evaluates pre-acquisition Gleamer algorithms, not the integrated DeepHealth portfolio. Whether results, when published, will generalize to the current product is uncertain. Evidence-driven buyers should wait for outcomes or require pilot agreements with independent audit rights.

Who it's for

Large integrated delivery networks seeking vendor consolidation are the best fit. A 10-hospital system imaging 1 million studies annually across radiology, cardiology, and oncology faces vendor sprawl if it adopts best-of-breed AI per specialty. DeepHealth offers one contract, one vendor relationship, one set of BAAs and security reviews. If the CMIO prioritizes administrative simplicity and is willing to pilot modules with thin evidence, this tool makes sense. Pilot agreements should mandate performance benchmarks and opt-out clauses if modules underperform.

RadNet-affiliated imaging centers are the natural adopters. Centers already using RadNet's RIS, PACS, and reading services can integrate DeepHealth with lower friction. The vendor knows the tech stack, the workflows, and the radiologist preferences. For these buyers, DeepHealth is an upsell, not a new vendor. However, even RadNet affiliates should request published outcomes and independent validation before assuming the AI improves diagnostic accuracy or efficiency.

This tool is not for evidence-driven CMIOs requiring published ROI before adoption. If your governance committee requires peer-reviewed sensitivity and specificity data, randomized controlled trials, or referenceable peer institutions before piloting AI, DeepHealth does not qualify. Choose Aidoc, Annalise, or Lunit, all of which have published validation. This tool is also not for smaller practices needing transparent per-study pricing. If your group images 20,000 studies annually and cannot commit to enterprise contracts, you will not get a quote. Finally, this tool is not for specialty departments wanting proven best-in-class performance in one modality. A breast imaging center should choose Lunit or standalone iCAD ProFound AI with published AUC data, not a bundled portfolio with unproven modules.

The verdict

DeepHealth is the largest radiology AI portfolio by breadth, not by validation. The March 2026 Gleamer acquisition created a multi-modality offering unmatched in scope, but the evidence base is alarmingly thin: one protocol paper, zero outcomes publications, zero clinician forum mentions. For a tool claiming cardiovascular, oncology, and neurology applications, this is a gamble disguised as scale.

If you are a large health system or RadNet affiliate prioritizing vendor consolidation and willing to pilot unproven modules with performance-based contract terms, evaluate carefully. Require independent audits, sensitivity thresholds, false-positive caps, and opt-out rights. Do not sign multi-year enterprise contracts without six-month pilots demonstrating workflow improvement and diagnostic accuracy gains. If you are a CMIO requiring published outcomes, referenceable peer institutions, or transparent pricing before adoption, wait. Choose validated alternatives: Aidoc for triage, Annalise for chest, Lunit for oncology, or specialty point solutions with Medicare codes and trial data.

The tool's thin evidence (one protocol paper, zero Reddit mentions) makes it high-risk for evidence-driven buyers. Vendor-owned AI raises conflict-of-interest questions when RadNet reads its own studies using its own algorithms. External adopters lack independent validation and peer references. Until DeepHealth publishes outcomes across its modules, demonstrates external adoption beyond RadNet's network, and offers transparent pricing tiers, this is a strategic consolidation play for large systems comfortable with uncertainty, not a proven leader for evidence-based radiology departments.