Aidoc Review
VisitAI-powered radiology triage for faster, smarter clinical decisions
Aidoc is an AI medical imaging analysis platform that helps radiologists detect and prioritize critical findings.
AI Panel Score
7.8/10
6 AI reviews
Reviewed
AI-powered radiology triage for faster, smarter clinical decisions
Aidoc is an AI medical imaging analysis platform that helps radiologists detect and prioritize critical findings.
AI Panel Score
6 AI reviews
Reviewed
Aidoc is a medical AI company that develops artificial intelligence solutions for radiology and clinical operations. Its core platform analyzes imaging studies in real time, automatically identifying and triaging critical findings so radiologists can prioritize the most urgent cases. The system is designed to work continuously across imaging modalities including CT scans, enabling detection of conditions such as intracranial hemorrhage, pulmonary embolism, aortic dissection, and vertebral fractures.
The platform integrates directly into existing Picture Archiving and Communication Systems (PACS) and radiology information systems, allowing it to fit into established hospital and imaging center workflows without requiring significant infrastructure changes. Alerts and notifications are surfaced to radiologists and care teams through familiar tools, reducing friction in adoption.
Aidoc positions itself not just as a radiology AI tool but as a broader clinical operations platform. Beyond image analysis, it offers care coordination capabilities, helping connect radiologists with referring physicians and enabling faster follow-up on incidental findings. This extends the platform's value beyond the reading room into broader hospital workflow management.
The product is primarily targeted at hospitals, health systems, and radiology groups looking to manage increasing imaging volumes while maintaining diagnostic quality. Aidoc holds FDA clearances for multiple clinical applications, which is a key differentiator in the regulated medical AI market.
Aidoc competes in the rapidly growing medical AI imaging space alongside companies such as Viz.ai, Annalise.ai, and Nanox. It has positioned itself as one of the more broadly deployed platforms in the sector, with reported use across a significant number of healthcare facilities globally.
The largest portfolio of FDA-cleared deep learning algorithms running on a single platform, covering pathologies across neuro, chest, vascular, breast, bone, and abdomen imaging.
Provides standardized quantitative assessments of detected pathologies, such as RV/LV Analysis and ASPECTS Scoring, seamlessly incorporated into the radiologist's workflow.
Monitoring infrastructure ensures consistent algorithm performance over time and supports rapid deployment and scaling of algorithms across single institutions and health networks.
Automatically applies multiple algorithms to flag suspected abnormalities and alert radiologists to urgent findings without requiring any additional clicks in their existing workflow.
Creates automated hub and spoke networks to improve disease awareness and standardize care pathways and patient transfers across institutions and networks.
Real-time mobile activation solution that enables radiologists to notify specialists of urgent cases while providing cross-department communication and access to imaging and clinical data.
Ensures patients are identified, captured, and followed throughout the patient lifecycle to track progress and improve outcomes beyond the point of initial diagnosis.
Aidoc's proprietary enterprise operating system that runs in the background to evaluate every relevant exam, orchestrate multiple AI applications simultaneously, and coordinate workflows across a facility's existing IT stack.
Provides a unified user interface for accessing results from both Aidoc's own algorithms and vetted third-party algorithm developers and OEMs deployed through the aiOS™ platform.
Seamlessly integrates with existing native workflow and IT infrastructure including PACS, EHR, RIS, and scheduling systems with custom configuration and minimal IT lift.
Enables communication between desktop and mobile workflows, allowing urgent AI-flagged findings to be surfaced on mobile devices for immediate specialist notification.
A structured framework designed to help healthcare facilities integrate AI into clinical practice as part of a scalable AI strategy.
Designed for smaller radiology practices processing up to ~100 studies per month. Pricing is not publicly listed and must be obtained by contacting Aidoc sales. Third-party estimates suggest costs around $500/month at this volume tier.
Suited for larger facilities and radiology departments processing over 1,000 studies per month. Pricing is not publicly listed and requires contacting sales. Third-party estimates suggest costs around $5,000/month at this volume tier.
Custom-priced solution for large hospital systems and multi-site enterprises. Pricing is fully sales-led and not publicly disclosed. Implementation costs may range from $20,000–$50,000 for enterprise deployments, per third-party estimates. Contact Aidoc sales for a quote.
Serious clinical AI with FDA clearances and real deployment breadth — not a demo.
“Aidoc's aiOS™ runs across neuro, chest, vascular, and more with 17+ FDA clearances on a single platform. Pricing is opaque, but the clinical depth and workflow integration are real.”
17 FDA clearances on one platform. That's not a feature list — that's a regulatory moat that Viz.ai and Annalise.ai haven't matched at that breadth. The aiOS™ runs continuously across PACS, EHR, and RIS with minimal IT lift, which means radiology ops teams aren't blocked waiting on IT for six months.
The tradeoff is pricing opacity. No public numbers, fully sales-led, third-party estimates put enterprise implementation at $20,000–$50,000 upfront. That's defensible for a health system. It's a barrier for a mid-size group practice that just wants to pilot pulmonary embolism triage before committing.
The BRIDGE Guidelines Framework and care coordination tools push this beyond a reading-room alert system into genuine clinical operations. That's the strategic bet worth making. Pilot at one facility, validate the worklist reprioritization impact, then decide on network rollout.
Third-party algorithm partner support via aiOS™ creates a platform moat that point solutions like Viz.ai can't easily replicate at this breadth.
FDA clearances and reported broad hospital deployment make this an easy board defense — no one gets fired for buying the most-cleared AI radiology platform.
Automated case prioritization integrates into existing workflows without added clicks, but enterprise implementation estimates of $20,000–$50,000 suggest the ramp isn't instant.
aiOS™ and care coordination tools advance clinical operations beyond cost savings — this changes how radiologists and specialists interact, not just how fast images get read.
Multi-year deployment across global health systems and 17+ FDA clearances signals a durable regulatory and commercial position, though no public funding data is available.
Health systems and radiology groups managing high imaging volume who need a proven, FDA-cleared AI platform that fits existing workflows.
You're a mid-size practice that needs transparent pricing and a fast self-serve pilot before committing.
Seventeen FDA clearances and an enterprise OS signal serious clinical infrastructure, not a point solution.
“Aidoc has built something closer to a clinical AI operating layer than a single-indication triage tool. The aiOS™ architecture, multi-specialty coverage, and third-party algorithm support suggest a platform designed for health system-scale deployment.”
The FDA clearance count matters clinically. Seventeen cleared algorithms across neuro, chest, vascular, and musculoskeletal means radiology leadership isn't betting on a single pathway — they're acquiring a durable triage infrastructure. The ASPECTS Scoring and RV/LV Analysis quantification features tell me someone who's actually worked in stroke and PE protocols shaped this product roadmap.
The aiOS™ platform is the strategic bet. Vendor-neutral, PACS/EHR/RIS-integrated, with third-party algorithm ingestion — if that architecture holds, adopting Aidoc in year one means you're building a clinical AI governance layer, not just a worklist sorter. The risk: full pricing opacity and implementation costs estimated at $20,000–$50,000 enterprise-side make budget justification to the CFO a recurring friction point.
Against Viz.ai, Aidoc's breadth wins on paper. Viz owns the neurovascular activation workflow more deeply, but Aidoc's multi-specialty span is clinically compelling for health systems managing volume across service lines, not just stroke centers.
Positioned ahead of single-indication competitors by breadth, with Viz.ai as the sharper competitor in neurovascular activation specifically.
Automated worklist reprioritization, ASPECTS Scoring, and mobile specialist notification map directly to how radiologists and ED care teams actually manage time-critical findings.
Native PACS, EHR, RIS, and scheduling integration with described minimal IT lift addresses the largest operational barrier to radiology AI adoption in health systems.
If aiOS™ becomes your clinical AI orchestration layer, vendor switching costs grow significantly by year two — that's a strategic commitment worth acknowledging at contract signing.
Multi-specialty algorithm portfolio plus care coordination and the aiOS™ operating layer shows platform-level thinking, not feature accumulation.
Large health systems and radiology groups managing high imaging volumes across multiple service lines who need a durable clinical AI governance layer.
You need a single-indication, fast-deploy neurovascular activation tool and want transparent per-use pricing.
$5K/month estimate at 1,000 studies — but no invoice to trust.
“Zero public pricing. Every number here comes from third-party estimates, not Aidoc's own pages.”
Third-party estimates put the mid-tier at $5,000/month — $60K/year for a 1,000-study-per-month facility. Enterprise implementation adds $20K–$50K upfront, per the same sources. Year 3 all-in for a mid-size health system: realistically $200K–$250K before seat expansion or add-on algorithms. That's not a budget line you can defend without a real quote.
The aiOS™ platform and 17+ FDA clearances are genuine differentiators. Viz.ai competes directly and has the same opaque pricing problem. Neither company lets procurement do homework without a sales call. The BRIDGE Guidelines framework and third-party algorithm support add TCO complexity — each vetted partner likely carries its own cost line.
Annual subscription is the implied term. No public auto-renewal window, no termination-for-convenience clause visible. That's a negotiation you need to win before signing. ROI framing around reduced time-to-diagnosis is clinically real but financially unmeasured in any public material.
Fully sales-led procurement with no self-serve trial or free tier; procurement friction is high by design.
Annual subscription implied; no public auto-renewal window or termination-for-convenience terms visible.
No pricing page exists; third-party estimates of $500–$5,000/month are the only public numbers.
Time-to-diagnosis improvement is the stated value driver, but no published ROI benchmarks or outcome data appear in the evidence.
Implementation estimates of $20K–$50K for enterprise exist, but add-on algorithm costs and overage rates are undisclosed.
Large health systems with dedicated procurement and IT teams that can negotiate enterprise terms.
You need budget certainty before board approval — the pricing opacity will stall your procurement cycle.
Aidoc's aiOS runs in the background so radiologists don't have to think about it
“Seventeen-plus FDA clearances across a single platform is a real clinical moat. The pricing opacity is a procurement headache, but the workflow integration story is credible.”
The 'no additional clicks' claim around Automated Case Prioritization is the thing I'd verify on day three. If worklist reprioritization actually surfaces intracranial hemorrhage and PE findings without pulling a radiologist out of their reading workflow, that's genuine friction removal. PACS integration that requires 'minimal IT lift' reads like marketing until implementation proves otherwise — but the aiOS™ architecture suggests someone thought about the hospital IT stack seriously, not as an afterthought.
Viz.ai competes directly here, particularly on stroke and PE pathways. Aidoc's differentiator is breadth: neuro, chest, vascular, breast, bone, abdomen on one platform versus point solutions. The BRIDGE Guidelines Framework also signals they've done enough real-world deployments to know that algorithm adoption fails without change management support. That's a clinician-facing insight, not a sales one.
The opaque pricing — third-party estimates put enterprise implementation at $20,000–$50,000 — means every budget cycle involves a sales call. No changelog is a gap; radiologists need to know when an algorithm updates. Care coordination via mobile is strong for specialist notification, but without API documentation, custom workflow extensions look difficult.
Automated worklist reprioritization with no added clicks is the right promise, but no changelog means algorithm updates are invisible to daily users.
No public docs link, no changelog, and the BRIDGE framework reads more like a consulting deliverable than a field guide radiologists would actually open mid-shift.
Mobile care coordination and real-time alerts reduce the radiologist-to-specialist notification loop, but sales-only pricing means procurement friction never fully disappears.
Third-party algorithm partner support and quantification tools like RV/LV Analysis and ASPECTS Scoring show real depth for advanced radiology workflows — not just triage flags.
Native PACS, EHR, RIS, and scheduling integration via aiOS™ is the most credible integration story in the category — fits existing radiology infrastructure without ripping anything out.
Health systems and radiology groups running high CT volumes who need multi-pathology triage across a single integrated platform.
Your radiology department needs transparent per-study pricing or a self-serve trial before committing to a sales-led procurement cycle.
Hospital-grade AI triage that actually fits into how radiology already works
“Aidoc's aiOS™ platform is doing serious clinical work — 17+ FDA clearances, real PACS integration, zero extra clicks for radiologists. The tradeoff is opacity: no public pricing, no changelog, no free trial.”
This isn't a tool you demo to a team of five. Aidoc is infrastructure — the kind that runs in the background, evaluates every relevant exam, and surfaces urgent pulmonary embolisms and intracranial hemorrhages before a radiologist even opens the worklist. The Automated Case Prioritization feature requires no additional clicks in existing workflows. That detail matters enormously. Adoption dies on friction, and they clearly know it.
Third-party estimates put mid-tier pricing around $5,000/month for 1,000+ studies. Enterprise implementation reportedly runs $20,000–$50,000. That's not a small commitment, and unlike Viz.ai, there's nothing public to benchmark against. Sales-led pricing at every tier means you won't know your number until you're already in the conversation.
The mobile workflow is a real feature — care coordination, specialist notifications, cross-department imaging access. Not read-only. That's better than most clinical tools manage. Three months in, the question is whether your IT team felt the integration or just signed off on it.
Automated Case Prioritization with zero extra clicks suggests someone designed this for people who are already overwhelmed — that's thoughtful daily UX.
PACS/EHR/RIS integration with minimal IT lift plus the structured BRIDGE framework suggests manageable ramp, but enterprise deployment complexity at $20,000–$50,000 implementation cost implies it's not trivial.
Mobile Workflow Communication enables real-time specialist alerts and imaging access — not a stripped-down companion app, an actual care coordination tool.
No free trial, no public pricing page, and no changelog — the BRIDGE Guidelines Framework helps, but first contact is entirely sales-gated.
Continuous Performance Monitoring infrastructure and 24/7 live support at higher tiers signal they've built for always-on clinical environments where downtime is not an option.
Mid-to-large hospital systems and radiology groups processing high imaging volumes who need always-on AI triage without rebuilding their existing PACS workflow.
You're a small practice that needs transparent pricing and a self-serve trial before committing to a sales process.
17 FDA clearances is real — the missing pricing page isn't.
“Aidoc has genuine regulatory moat and PACS-native integration that Viz.ai and Annalise.ai can't easily replicate at this breadth. But the all-contact pricing and missing changelog are tells I don't love in a 2024 vendor.”
Three flags before I dig in. One: no pricing page — enterprise-only opacity on a platform that claims SMB tiers. Two: no changelog visible. For a clinical AI product, shipping cadence is the whole story. Three: 'ALWAYS ON AI' as the H1 is the kind of superlative that ages poorly. That said, 17+ FDA clearances across neuro, chest, vascular, and abdominal pathologies is genuinely hard to replicate. That's regulatory moat, not marketing.
The aiOS™ platform running third-party algorithms alongside native ones is smart positioning — it makes Aidoc stickier without requiring them to win every algorithm race. ASPECTS Scoring and RV/LV Analysis are specific, clinical, and not table stakes. That's differentiation I can name. Third-party estimates put mid-tier cost around $5,000/month — plausible, but unverifiable without a real pricing page.
Exit portability worries me. Deep PACS and EHR integration means leaving is painful. That's by design. If direction shifts in 18 months — and healthcare AI vendors do shift — you're negotiating, not migrating.
17+ FDA clearances across six specialties on one platform, plus third-party algorithm support, puts clear distance from Viz.ai's narrower stroke-focused stack.
Deep EHR, PACS, and RIS integration with custom onboarding means migration off aiOS™ would require significant IT effort — by design.
No public funding data visible, no changelog, but enterprise health system contracts and regulatory depth suggest a real operating business — not a Series A science project.
No pricing page, no changelog, and 'largest portfolio' claim without a specific count — the docs indicate more aspiration than transparency.
FDA clearance accumulation and PACS-native integration matches patterns from healthcare AI survivors, not the ones that folded.
Mid-to-large hospitals or health systems managing high CT volumes who need FDA-cleared triage across multiple specialties on one platform.
You need transparent pricing, a vendor with visible shipping cadence, or a clean exit path in under 18 months.
Common questions answered by our AI research team
Yes, the aiOS™ platform effortlessly integrates with native workflow and IT infrastructure, including PACS, EHR, RIS, and scheduling, with custom configuration described as requiring minimal IT lift.
The content references the 'largest portfolio of FDA-cleared algorithms running on a single platform' but does not specify an exact number of FDA-cleared algorithms. The platform does support third-party algorithm developers through a vetted partners program, and the aiOS™ platform provides a unified user interface for accessing results from both Aidoc and third-party algorithms.
Founded
2016Pricing
Contact for pricingAidoc is a Tel Aviv-based healthcare AI company providing radiology tools that flag critical findings in medical imaging.
