AI diagnostic platforms don't replace a radiologist, pathologist, or cardiologist's judgment — the FDA clearances behind every tool in this comparison are for assistive, not autonomous, use. What they do is compress the time between a scan landing in a queue and a clinician seeing the finding that actually matters.
The seven platforms below split across three specialties: Viz.ai and Aidoc triage radiology cases across the whole hospital; PathAI and Paige analyze pathology slides for cancer diagnosis; Cleerly and HeartFlow quantify cardiac imaging. None publish list pricing — every deal here is an individually negotiated enterprise contract with a health system.
We compared FDA clearance depth, EHR/PACS integration, deployment model, and how each platform frames its role in the broader case-triage or diagnostic workflow. Full breakdown below, including a six-column comparison table.
Last updated: August 17, 2026
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Quick summary: We compared Viz.ai, Aidoc, PathAI, Paige, Qure.ai, Cleerly, and HeartFlow on FDA clearance, EHR/PACS integration, and deployment model. Aidoc wins overall for the largest single-platform portfolio of FDA-cleared algorithms across specialties; Viz.ai is the strongest fit for hospital-wide radiology triage with LLM-assisted patient summaries.
Why You Need AI Medical Diagnostic Platforms
- Cut the time between a critical finding and a clinician seeing it. Viz.ai and Aidoc route time-sensitive cases like strokes and hemorrhages to the right specialist within seconds of the scan completing, not hours later in a standard queue.
- Catch findings a busy read might miss. Automated flagging on every scan means subtle findings get a second, consistent look regardless of how many other cases are stacked up that shift.
- Standardize diagnosis quality across sites. A health system running the same AI algorithm across five hospitals gets a consistent baseline read, not one that varies by which radiologist or pathologist is on shift.
- Quantify disease instead of just flagging it. Tools like Cleerly and HeartFlow don't just say "abnormal" — they measure plaque volume or flow reserve with numbers a cardiologist can track over time.
- Free up specialist time for the cases that need it most. Automated triage of low-priority, clearly-normal scans means radiologists and pathologists spend their attention on the ambiguous, high-stakes cases.
How We Evaluated These Tools
We scored each platform on four criteria: breadth and depth of FDA clearance (single algorithm versus a portfolio, and whether clearance extends to primary diagnosis versus triage/decision support), EHR/PACS integration depth, deployment model (cloud versus on-prem flexibility), and specialty focus. Every clearance and feature claim comes from each vendor's own site as of August 2026; where FDA clearance status wasn't confirmed on the specific pages checked, that's stated honestly rather than assumed.
Best 7 AI Medical Diagnostic Platforms in 2026
1. Aidoc
Aidoc's pitch is breadth: it calls itself the largest portfolio of FDA-cleared algorithms running on a single platform, spanning radiology, cardiology, neurovascular, and vascular care rather than one specialty.
Pricing: Not published. Aidoc is sold as an enterprise contract to hospitals and health systems, individually negotiated; the company cites a benchmark 3-5x ROI claim rather than a list price.
Top features:
- 24/7 fully automated monitoring and real-time case analysis
- Largest single-platform portfolio of FDA-cleared algorithms cited by the company
- aiOS, a proprietary enterprise operating system for unified AI deployment
- Multi-specialty coverage: radiology, cardiology, neurovascular, vascular
- Real-time care-coordination notifications with image and data sharing
- AI-powered mining of radiology reports to route findings to the right team
Pros:
- Broadest FDA-cleared algorithm portfolio on one platform among the tools compared here
- Multi-specialty coverage beyond radiology alone (cardiology, neurovascular, vascular)
- Fast stated implementation timeline (2-3 weeks) into existing hospital IT infrastructure
Cons:
- No public pricing at all, even a starting range — every deal requires a sales conversation
- ROI benchmark cited (3-5x) is from 2022 company data, not independently verified
AI/MCP Integration: Not documented. No official or community MCP server was found for Aidoc as of this writing.
API Integration: Not explicitly documented on the platform page; integration is described as working within existing hospital IT and EHR infrastructure.
Cloud Based: Yes.
Platforms: Web-based, integrated into existing hospital PACS and EHR systems.
Best for: health systems wanting one AI platform to cover radiology, cardiology, and vascular triage together.
Editor score: 4.4/5 — the broadest FDA-cleared algorithm portfolio here, docked for zero pricing transparency.
2. Viz.ai
Viz.ai layers an LLM-derived auto-summary of a patient's medical history on top of its core stroke and disease-triage engine, a distinct feature none of the other six platforms mention.
Pricing: Not published. Viz.ai is sold as an enterprise contract; the company reports it is deployed live in more than 1,400 hospitals.
Top features:
- Auto-orchestration that routes the right algorithm to the right image automatically
- LLM-derived auto-summary of a patient's past medical history
- Cited 99.11% sensitivity and 99.3% specificity performance figures
- Auto-detection and critical-insight delivery to the entire care team within seconds
- Vendor-agnostic HL7 and FHIR integration standards
- Turnkey cloud implementation with no on-prem hardware required
Pros:
- Only platform in this comparison that layers an LLM-generated patient history summary onto imaging triage
- Cited sensitivity/specificity figures are unusually specific for a marketing page
- Vendor-agnostic HL7/FHIR integration plus a named Redox partnership for EHR connectivity
Cons:
- No public pricing published anywhere on the site
- Sensitivity/specificity figures are self-reported by the company, not independently cited to a peer-reviewed source on this page
AI/MCP Integration: Not documented. No official or community MCP server was found for Viz.ai as of this writing.
API Integration: Yes, implied — vendor-agnostic HL7 and FHIR integrations plus a Redox integration partnership are explicitly named.
Cloud Based: Yes, turnkey with no on-prem hardware required.
Platforms: Web-based, integrated with Epic, Cerner, and Meditech EHR systems.
Best for: hospitals wanting hospital-wide radiology triage with AI-generated patient-history context built in.
Editor score: 4.5/5 — the most feature-differentiated platform here, thanks to its LLM-summary layer.
3. PathAI
PathAI's AISight Dx became FDA-cleared for primary diagnosis in pathology in mid-2025 — not just decision support — putting it ahead of most imaging-AI platforms that remain triage-only.
Pricing: Not published. PathAI is sold as an enterprise digital-pathology platform to labs and health systems.
Top features:
- AISight Dx, FDA 510(k)-cleared for primary diagnosis in digital pathology
- Cloud-native, open enterprise workflow platform for case and image management
- Named algorithm catalog: ArtifactDetect, TumorDetect, AIM-Tumor Cellularity, AIM-PDL1, and more
- PathAssist AI-assisted diagnostic tool
- AIM-MASH, the first AI tool to receive FDA qualification for MASH clinical trials
- CE-IVDR marked version available for the EU market alongside the US FDA-cleared version
Pros:
- AISight Dx clearance covers primary diagnosis, a step beyond triage/decision-support-only clearances
- Deep, named algorithm catalog spanning tumor detection, biomarker quantification, and multiple cancer subtypes
- Both US FDA-cleared and EU CE-IVDR-marked versions available
Cons:
- No public pricing published anywhere on the site
- Platform complexity (11+ named algorithm products) may require significant onboarding for smaller labs
AI/MCP Integration: Not documented. No official or community MCP server was found for PathAI as of this writing.
API Integration: Not documented on the pages checked for this research pass.
Cloud Based: Yes, cloud-native architecture.
Platforms: Web-based, with regional US-FDA and EU CE-IVDR versions.
Best for: pathology labs wanting FDA-cleared primary-diagnosis AI, not just decision support.
Editor score: 4.3/5 — the deepest algorithm catalog here, backed by a genuine primary-diagnosis clearance.
4. Paige
Paige holds the distinction of the first FDA-approved AI in pathology, and backs it with three separate FDA Breakthrough Device designations across its breast and pan-cancer detection suites.
Pricing: Not published. Paige is sold as an enterprise digital-pathology platform, typically alongside partner integrations rather than standalone.
Top features:
- Paige Prostate Suite: detection, grading, and perineural invasion identification
- Paige Breast Suite: cancer detection, mitosis identification, HER2 expression measurement
- Paige GI Suite covering esophagus, stomach, colon, pancreas, and more
- Paige PanCancer Detect across 21 biopsy and 25 resection tissue types
- HER2Complete, an AI assay measuring HER2 expression directly from H&E samples
- FullFocus whole-slide viewer and FullFolio image management system
Pros:
- First FDA-approved AI in pathology, with Prostate Detect cleared for primary diagnosis
- Three FDA Breakthrough Device designations across breast and pan-cancer detection
- Eight named digital-pathology integration partners, including PathAI and Roche
Cons:
- No public pricing published anywhere on the site
- Overlapping algorithm scope with PathAI (both cover prostate, breast, and pan-cancer detection) means buyers should compare clearance specifics closely
AI/MCP Integration: Not documented. No official or community MCP server was found for Paige as of this writing.
API Integration: Not documented on the pages checked for this research pass.
Cloud Based: Yes.
Platforms: Web-based Paige Platform, with integrations into eight named digital-pathology partner systems.
Best for: pathology labs prioritizing the deepest FDA regulatory track record of any platform in this comparison.
Editor score: 4.2/5 — the strongest regulatory pedigree here, in a crowded field against PathAI's similar scope.
5. Qure.ai
Qure.ai leans hardware-agnostic and mobile-first, with a companion app that puts flagged findings on a clinician's phone the moment a scan is acquired, rather than requiring a workstation login.
Pricing: Not published. Qure.ai is sold as an enterprise contract, with hardware-agnostic deployment cited as a selling point for lower-resource settings.
Top features:
- qER: FDA-cleared for intracranial hemorrhage detection at 97% cited sensitivity
- Guidelines-based clinical decision support for stroke and TBI treatment planning
- Real-time high-priority alerts on stroke diagnosis
- Qure App for mobile access to images immediately upon acquisition
- Multi-hospital data-sharing and care-coordination features
- Hardware-agnostic deployment across varied CT scanner types
Pros:
- Hardware-agnostic design broadens deployment to lower-resource hospital settings other platforms may not target
- Mobile companion app gets findings to a clinician's phone immediately, not just a workstation
- CE Class IIb certification alongside FDA clearance for international deployment
Cons:
- FDA clearance is explicitly scoped to bleeds only for qER, narrower than the multi-algorithm portfolios at Aidoc or Viz.ai
- No public pricing published anywhere on the site
AI/MCP Integration: Not documented. No official or community MCP server was found for Qure.ai as of this writing.
API Integration: Not explicitly documented; integration is described as working with PACS systems and enabling multi-hospital data sharing.
Cloud Based: Yes.
Platforms: Web-based, plus the Qure App for mobile access.
Best for: health systems in lower-resource or hardware-varied settings needing mobile-first stroke/TBI triage.
Editor score: 4.0/5 — genuinely differentiated hardware-agnostic and mobile design, with narrower FDA clearance scope.
6. Cleerly
Cleerly is the only platform here focused on quantifying coronary plaque itself, vessel by vessel, rather than flagging a binary abnormal/normal result — a meaningfully different clinical use case than the triage tools.
Pricing: Not published. Cleerly operates a provider-facing platform (Cleerly Labs) alongside a separate payor-coverage lookup tool, suggesting a mix of institutional and reimbursement-linked pricing not disclosed publicly.
Top features:
- Vessel-by-vessel coronary plaque quantification and stenosis scoring
- Vascular remodeling score calculation
- Support for next-generation hardware including photon-counting CT
- Validation cited against QCA, IVUS, NIRS, and myocardial perfusion imaging
- Cleerly Labs, a dedicated web-based provider platform
- Separate payor-coverage lookup tool for reimbursement verification
Pros:
- The only platform in this comparison that quantifies plaque volume and vessel-level detail rather than a binary flag
- Validated against multiple independent diagnostic reference standards (QCA, IVUS, NIRS)
- Dedicated payor-coverage lookup tool signals real attention to reimbursement friction
Cons:
- FDA clearance status wasn't confirmed on the pages checked for this research pass — verify directly with Cleerly before assuming clearance scope
- No public pricing published anywhere on the site
AI/MCP Integration: Not documented. No official or community MCP server was found for Cleerly as of this writing.
API Integration: Not documented on the pages checked for this research pass.
Cloud Based: Yes, via Cleerly Labs.
Platforms: Web-based (Cleerly Labs), with a separate patient-facing portal.
Best for: cardiology practices wanting quantified, trackable coronary plaque analysis rather than a binary flag.
Editor score: 3.9/5 — a genuinely distinctive quantification approach, docked for unconfirmed FDA clearance detail.
7. HeartFlow
HeartFlow pioneered non-invasive FFR-CT analysis via a De Novo FDA pathway back in 2014, and still cites a fast, specific 90-minute median turnaround from scan to diagnostic pathway.
Pricing: Not published. HeartFlow FFRct Analysis is billed through a mix of institutional contracts and clinical reimbursement pathways.
Top features:
- Computational fluid dynamics combined with deep learning algorithms
- Patient-specific, interactive 3D anatomical and physiological reconstruction
- Fractional flow reserve (FFR) analysis from standard CT angiography, avoiding invasive catheterization
- 90-minute median turnaround cited from scan to diagnostic pathway
- Commercially available in the US, EU, UK, Japan, and Canada
- ISO 13485 and HITRUST certification
Pros:
- Longest FDA regulatory track record here, cleared via a De Novo pathway back in 2014 as a novel device category
- Avoids invasive catheterization by deriving FFR non-invasively from CT imaging
- Broadest international commercial availability cited among the platforms in this comparison
Cons:
- No public pricing published; reimbursement pathway can vary meaningfully by region and payor
- Narrower clinical scope than multi-algorithm platforms — HeartFlow is purpose-built for coronary FFR analysis specifically
AI/MCP Integration: Not documented. No official or community MCP server was found for HeartFlow as of this writing.
API Integration: Not documented on the pages checked for this research pass.
Cloud Based: Yes, web-based application.
Platforms: Web-based, commercially available across the US, EU, UK, Japan, and Canada.
Best for: cardiology practices wanting non-invasive FFR analysis with the longest regulatory track record in this comparison.
Editor score: 4.1/5 — the most established regulatory history here, in a narrower clinical niche than the multi-algorithm platforms.
Comparison Table
| Tool | Best For | Starting Price | Standout Feature | AI-MCP Support | API Integration |
|---|---|---|---|---|---|
| Aidoc | Multi-specialty hospital-wide AI triage | Custom (not published) | Largest FDA-cleared algorithm portfolio | Not documented | Not documented |
| Viz.ai | Hospital-wide radiology triage + LLM context | Custom (not published) | LLM-derived patient-history auto-summary | Not documented | Yes, HL7/FHIR |
| PathAI | FDA-cleared primary-diagnosis pathology | Custom (not published) | AISight Dx cleared for primary diagnosis | Not documented | Not documented |
| Paige | Deepest pathology regulatory track record | Custom (not published) | First FDA-approved AI in pathology | Not documented | Not documented |
| Qure.ai | Lower-resource, mobile-first stroke triage | Custom (not published) | Hardware-agnostic + mobile companion app | Not documented | Not documented |
| Cleerly | Quantified, trackable coronary plaque analysis | Custom (not published) | Vessel-by-vessel plaque quantification | Not documented | Not documented |
| HeartFlow | Non-invasive FFR analysis, longest track record | Custom (not published) | De Novo FDA-cleared FFR-CT since 2014 | Not documented | Not documented |
How to Choose an AI Medical Diagnostic Platform
- Specialty focus first: Viz.ai and Aidoc cover hospital-wide radiology triage; PathAI and Paige are pathology-specific; Cleerly and HeartFlow are cardiac-imaging-specific. Match the platform to the department, not the other way around.
- Clearance depth: PathAI's AISight Dx and Paige's Prostate Detect are cleared for primary diagnosis, a higher regulatory bar than triage/decision-support-only clearances like Qure.ai's qER (bleeds only).
- Deployment flexibility: Qure.ai's hardware-agnostic design is worth prioritizing if your imaging hardware fleet is mixed or older; Viz.ai and Aidoc both cite turnkey cloud deployment with minimal on-prem requirements.
- Algorithm breadth vs. depth: Aidoc and PathAI both offer broad, multi-algorithm portfolios; Cleerly and HeartFlow are narrower but go deeper on one specific clinical question (plaque volume, FFR).
- EHR/PACS integration: Viz.ai explicitly names Epic, Cerner, and Meditech compatibility plus a Redox integration partnership — confirm your specific EHR is supported before shortlisting any platform.
- Reimbursement pathway: Cleerly's dedicated payor-coverage lookup tool and HeartFlow's established reimbursement history are worth weighing if patient billing clarity matters as much as clinical accuracy.
- Pricing model: none of the seven platforms publish list pricing — budget for a multi-stakeholder procurement process (IT, clinical leadership, finance) rather than a straightforward quote request.
Why There's No TCO Example Here
Every one of the seven platforms compared here is sold through individually negotiated enterprise contracts with hospitals and health systems — none publish list pricing, per-scan rates, or site-license figures publicly. Health-system AI procurement typically bundles licensing with implementation services, EHR/PACS integration work, and sometimes reimbursement-linked pricing tied to clinical outcomes, none of which are comparable across vendors from public information alone. Rather than fabricate a plausible-sounding cost estimate, we're skipping the worked example for this category and recommending buyers request quotes directly, informed by the clearance depth and specialty fit covered in the sections above.
Final Thoughts
Aidoc is the strongest all-around pick if your health system wants one platform covering the broadest set of FDA-cleared algorithms across radiology, cardiology, and vascular care. Viz.ai is the closer runner-up, particularly if the LLM-generated patient-history context is valuable to your triage workflow.
For pathology specifically, PathAI's primary-diagnosis clearance on AISight Dx and Paige's Breakthrough Device track record are both strong, closely-matched choices — the right pick depends on which named algorithm suite (tumor types, biomarkers) fits your lab's caseload. For cardiac imaging, Cleerly and HeartFlow answer genuinely different clinical questions (plaque quantification versus flow reserve) and are worth evaluating as complements rather than competitors.