AI-powered medical imaging for tuberculosis, lung cancer, stroke and chest X-ray reporting
Qure.ai is a medical imaging AI platform for radiology-based diagnosis and care coordination in healthcare systems.
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The website highlights that its AI models are designed to work with a range of imaging hardware, including older X-ray machines, which it says allows healthcare facilities to use existing equipment rather than requiring new infrastructure. Qure.ai references WHO evaluations of its TB screening tools, FDA clearances for some of its imaging AI products, and real-world studies measuring clinical impact, such as a published study on stroke treatment speed. It also describes work on AI biomarkers intended for use in clinical trials, in addition to its point-of-care diagnostic tools.
Qure.ai's customers include hospitals, national tuberculosis programs, ministries of health, and pharmaceutical companies. Cited partners and clients include the NHS (Greater Glasgow and Clyde, and Nottingham University Hospitals), Medtronic, AstraZeneca, Merck's Global Health Innovation Fund, Medica Group, and Lesotho's Ministry of Health. Pricing is not published on the website and is arranged directly with healthcare providers, health systems, and government programs; the product falls in the broader category of medical imaging AI alongside vendors such as Aidoc and Viz.ai.
qER is a radiological computer-aided triage and notification tool that flags suspected intracranial hemorrhage, mass effect, midline shift, and cranial fracture on head CT images to accelerate emergency care.
Qure.ai's qXR uses deep learning to classify chest X-rays as normal or abnormal and detect abnormalities in the lungs, pleura, mediastinum, bones, diaphragm, and heart in under a minute.
qXR-BT is FDA 510(k) cleared automated radiological image processing software that facilitates confirmation of breathing tube position relative to anatomical landmarks on adult chest X-rays.
qXR-LN is FDA 510(k) cleared computer-aided detection and localization software that identifies and marks regions suspicious for pulmonary nodules on frontal chest radiographs as a second reader.
qCT LN Quant provides advanced CT-based nodule quantification and tracking, including Lung-RADS and Brock score risk stratification with Fleischner guideline-based follow-up recommendations.
qER-Quant automatically labels, visualizes, and quantifies segmentable brain structures—intracranial hyperdensities, lateral ventricles, and midline shift—from non-contrast head CT images.
Qure.ai's Stroke Care Suite combines the qER algorithm with the Qure App to send high-priority AI-powered notifications that activate specialist teams as soon as a stroke is detected.
qTrack is an end-to-end disease management platform that gives stakeholders ready access to patient information, diagnosis tracking, test results, and real-time progression monitoring across departments.
Qure.ai onboarded its lung cancer workflow suite, including qXR-LN, qCT-LN Quant, qTrack, and qER, onto Microsoft's Precision Imaging Network for streamlined single-point deployment to U.S. hospitals.
qXR integrates directly into the radiology workflow, pushing visual and free-text outputs back to PACS and sending HL7 messages to the RIS for reporting radiologists to review.
The Qure.ai app is a cloud-based image viewing application supporting multi-modality CT, MR, and X-ray review on mobile and desktop devices, including HIPAA-compliant messaging and critical patient alerting.
Seven Qure.ai products—qXR-LN, qER (V2.0), qER-Quant, qCT-Quant, qXR-BT, qXR-PTX-PE, and qXR-CTR—are US FDA 510(k) cleared. CE marking covers a separate set: qXRv3.1 and qERv2.0 under Class IIa (EU MDD), and qXRv4.0, qER-NCCT, qCT and qMSK under Class IIb (EU MDR).
Qure.ai does not publish list pricing; it sells its AI radiology solutions (e.g., qXR, qER, qCT, qTrack) directly to hospitals, health systems, and government/enterprise buyers via custom, sales-led contracts, typically priced per scan volume, per site, or as an enterprise license negotiated with the vendor.
FDA-cleared imaging AI with real regulatory depth, but you'll never see a price until legal gets involved.
“Qure.ai stacks multiple FDA 510(k) clearances and WHO evaluations across TB, lung cancer, and stroke tools. Pricing is fully custom, so speed to contract depends entirely on your procurement team.”
Seven FDA-cleared products named on their regulatory page, and by their own newsroom 26 cleared indications across nine. qXR, qER, qCT-Quant, qXR-LN. That's not a startup with one demo, that's a company that's been through the regulatory wringer repeatedly.
Two things stand out. One: it works with old X-ray hardware, so hospitals skip capital spend. Two: it's on Microsoft's Precision Imaging Network for deployment, which suggests real integration muscle, not just an API doc.
No pricing page, no free trial, sales-led only. Fine for a hospital system, painful if you're a smaller clinic wanting to test before a six-month procurement cycle. Aidoc and Viz.ai play the same field — Qure.ai's edge is breadth across TB, lung cancer, and stroke rather than single-condition depth. Pilot at one site, measure turnaround time, then negotiate enterprise terms.
Breadth across TB, lung cancer, and stroke in one platform, plus Microsoft Precision Imaging Network deployment, differentiates from single-condition tools.
FDA/CE clearances de-risk clinical use, but no published data portability or contract terms anywhere on the site.
Works with existing X-ray hardware per their own Q&A, cutting infrastructure lead time, but sales-led onboarding adds friction.
Detects conditions clinicians can't reliably catch at scale manually — this is new capability, not just cheaper reads.
Multiple FDA 510(k) clearances across seven named products, plus separate EU MDR CE marking, signal active and sustained regulatory shipping.
Hospital systems or national health programs needing multi-condition imaging AI with regulatory clearance already in hand.
Skip if you need transparent per-seat pricing or a self-serve trial before looping in procurement.
Regulatory-grade radiology AI with a three-disease focus that matches how public health systems actually triage risk.
“Qure.ai's FDA-cleared, WHO-evaluated portfolio spans TB, lung cancer, and stroke with real workflow integration, not a single-point algorithm demo. The gap is pricing opacity, which matters when you're building a multi-year capital plan.”
qXR-LN and qER carry FDA 510(k) clearance, not just a CE mark, and the WHO evaluation on TB screening tells me their evidence pipeline is built for regulator scrutiny, not just a validation paper. That's the difference between a pilot tool and something I can put in front of my radiology chiefs and public health partners without a fight.
The hub-and-spoke stroke suite and qTrack care coordination platform show they understand that imaging AI is worthless if it doesn't change what happens in the next 20 minutes of clinical workflow. Compatibility with older X-ray machines matters enormously for TB programs in resource-constrained settings — that's the deployment reality most vendors ignore.
My hesitation: no published pricing, sales-led contracts only, and per-scan-volume terms I can't model until legal is already in the room. Deployment across 5,500+ sites in 105+ countries suggests real staying power, but locking a health system into qXR, qER, and qCT as one bundled workflow is a three-year architectural commitment I'd want capped and reviewed annually.
Sits alongside Aidoc and Viz.ai in imaging AI but differentiates on TB and global health program deployment rather than U.S. acute-care focus alone.
TB, lung cancer, and stroke coverage plus old-hardware compatibility matches actual public health and hospital triage patterns.
PACS/RIS push-back, HL7 messaging, and Microsoft Precision Imaging Network onboarding show real workflow embedding, not a standalone viewer.
5,500+ site deployment signals durability, but bundled multi-tool adoption is a multi-year commitment without published exit terms.
FDA clearances across qXR-LN, qER, qCT-Quant, and qXR-BT show regulatory depth beyond a single flagship algorithm.
Health systems and national TB or stroke programs needing regulatory-cleared imaging AI that works with existing hardware.
You need transparent, self-serve pricing to benchmark against other imaging AI vendors before engaging procurement.
No price on the site. Zero. Everything's a call with sales.
“Qure.ai sells qXR, qER, and qTrack via custom enterprise contracts, not list price. Strong regulatory story — FDA 510(k), CE, WHO evaluation — but the finance model is a black box.”
No pricing page. Contact sales, per the site — that's the whole model. Deployed at 5,500+ sites in 105+ countries, per their own numbers, but volume doesn't tell you the invoice.
Pricing is per-scan-volume or per-site, negotiated. That means TCO at year 3 depends entirely on scan growth you don't control. A hospital scaling CT volume 20% a year could see costs climb without a published overage rate — the real risk isn't the contract price, it's the usage curve.
Competitors named in the category — Aidoc, Viz.ai — also sell sales-led, so this isn't unusual. It's category norm, not a red flag specific to Qure.ai. But procurement teams should budget for PACS integration costs and multi-site negotiation time. The portfolio spans TB, lung cancer, and stroke across seven FDA-cleared products — bundling across them might be the actual savings lever, not list price.
Enterprise sales-led onboarding across hospitals, ministries of health, and pharma implies real procurement cycles, not self-serve.
No renewal or term-length terms published; evidence silent, category norm is multi-year enterprise lock-in.
No published pricing; site confirms custom contracts only, per-scan or per-site.
Cited real-world stroke treatment-speed study and WHO evaluation give measurable clinical anchors, unusual for this category.
Volume-based pricing means 3-year cost scales with scan growth — hard to forecast without a rate card.
Hospital systems and national health programs that can run a multi-month procurement cycle.
You need a quotable price before looping in finance.
Integration docs are specific down to the status codes, but nothing on the main site links to them.
“qXR and qER push results back into PACS and the RIS over HL7, and a token-authenticated REST endpoint is documented for pulling them directly. The catch is discovery — that documentation lives on a subdomain nothing links to, and the app release notes stop at version 3.1.6 in May 2023.”
qXR reads a chest film in under a minute and pushes free-text and HL7 back to the RIS. Right integration point — nobody wants a second viewer open on a night shift. qER flags intracranial hemorrhage and midline shift on head CT, then alerts the stroke team through the Qure.ai App.
Integration teams get more than the marketing site lets on. A REST Platform API is documented at documentation.qure.ai: DICOM in, a token-authenticated GET on the series UID to pull results, triage_status of Routine or Critical, 202 and 409 handling. But nothing in the site navigation points there, and the App release notes stop at version 3.1.6, May 2023.
Seven products hold US FDA 510(k) clearance, qXR-LN and qER V2.0 among them; CE marking covers a different list, qXRv4.0 and qCT under Class IIb. qCT-Quant's Lung-RADS and Brock scoring is the piece a tumor board actually reuses across follow-up scans.
Sub-minute chest X-ray triage, HL7 push and a documented results endpoint make the daily shape readable before purchase, though there is still no trial access.
The Platform API reference is specific down to auth headers and status codes, but it lives on a subdomain nothing links to and the app release notes end in May 2023.
In-workflow friction is low, but unpublished pricing and sales-led contracts push the friction onto procurement.
qCT-Quant's Lung-RADS and Brock stratification plus qER-Quant's brain structure quantification go past flag-and-forget triage into longitudinal case management.
Results land in PACS and the RIS over HL7, a REST endpoint exists for pulling them, and Microsoft Precision Imaging Network onboarding gives US hospitals a single deployment path.
Radiology teams who need AI findings delivered inside an existing PACS workflow.
Teams who need to trial the software before opening a procurement cycle.
This isn't a dashboard you log into, it's a workflow bolted into your radiology department.
“Qure.ai lives inside PACS and hospital pipelines, not in a browser tab you check daily. That makes normal software-review questions like onboarding and mobile parity almost beside the point.”
Most of what I judge — empty states, first ten minutes, spinners — barely applies here. This is qXR reading chest X-rays in under a minute, qER flagging brain bleeds, qTrack coordinating stroke cases across hub-and-spoke hospitals. It sits inside PACS and RIS, pushes HL7 messages, and gets used by radiologists mid-shift, not evaluated by someone poking around a free trial. There isn't one.
What's real: FDA 510(k) clearance on several products, WHO evaluation on the TB tools, deployment across 5500+ sites in 105+ countries per their own numbers. That's not demo glow, that's regulatory paperwork you can't fake.
The tradeoff: no published pricing, no trial, sales-led contracts only, so you're buying on trust and reference calls, not hands-on testing like you'd get with a Viz.ai or Aidoc eval. Fine for a hospital system with a procurement team. Rough if you just want to try it.
Integrates directly into PACS/RIS workflow rather than a standalone app, which is the right kind of unglamorous.
Designed to work with existing older X-ray hardware, lowering the infrastructure learning curve, though clinical adoption still needs workflow training.
Dedicated mobile app supports multi-modality CT/MR/X-ray review with HIPAA-compliant alerting, real functionality not just a companion viewer.
No trial and sales-led custom contracts mean onboarding is a procurement process, not a ten-minute test drive — the integration docs exist, but on an unlinked subdomain you have to go find.
FDA 510(k) clearance across qXR-LN, qER V2.0 and qCT-Quant, with EU MDR CE marking on a separate line-up, is a stronger reliability signal than any spinner or autosave test.
Hospital systems, national TB programs, or health ministries needing regulatory-cleared imaging AI at scale.
You want to self-serve test a tool with a free trial before involving procurement.
26 FDA-cleared indications across nine products, and a release log that stopped in May 2023.
“Their own US regulatory page names seven cleared products; the newsroom counts 26 cleared indications across nine. The docs portal is real but unlinked, and its release notes stop at version 3.1.6 in May 2023.”
Their own US regulatory page names seven 510(k) cleared products — qXR-LN, qER V2.0, qER-Quant, qCT-Quant, qXR-BT, qXR-PTX-PE, qXR-CTR. That undersells them. The newsroom counts 26 cleared indications across nine products. CE marking is a separate line-up: qXRv4.0, qER-NCCT, qCT and qMSK under Class IIb. Two lists, not one.
The counters are softer. 45M+ lives, 105+ countries, 5,500+ sites — vendor-published, no auditor named. Category norm across imaging AI, Aidoc and Viz.ai included, but I'd want it in the contract.
There is a docs portal, with a REST Platform API reference and on-prem deployment specs. Nothing on the marketing site links to it. However, the app release notes stop at version 3.1.6 in May 2023 — three years of silence in the one log that would show cadence.
26 cleared indications spanning TB, lung nodules, stroke and breathing-tube placement is a far broader portfolio than the single-purpose triage products it sits beside.
A documented REST Platform API and on-premise deployment specs help, but qXR pushes into PACS and sends HL7 to the RIS, so removal is a workflow rebuild.
FDA clearances kept arriving through February 2026, but the public app release notes stop at version 3.1.6 in May 2023, so cadence is unverifiable from the log.
The regulatory page understates their own count — 26 cleared indications across nine products — while the 45M+ lives and 5,500+ sites counters stay vendor-published with no auditor named.
26 FDA-cleared indications across nine products, plus a separate CE-marked line-up including qXRv4.0 and qMSK, back the clinical claims with outside sign-off.
Health systems that need FDA-cleared imaging AI running inside their existing PACS workflow.
Buyers who need independently audited deployment numbers before signing a multi-year contract.
Common questions answered by our AI research team
Yes. Qure.ai's technology fits all kinds of X-ray machines, new or old, letting facilities use existing hardware while getting immediate AI-driven results for conditions like TB.
Qure.ai's AI detects and manages tuberculosis, pediatric tuberculosis, silicosis, lung cancer, and stroke, using chest X-rays and CT scans across a global network of 5500+ sites in 105+ countries.
Yes. Qure.ai offers WHO-evaluated AI solutions supporting clinicians in fast, accurate diagnosis and treatment decisions for Tuberculosis, Silicosis, and Pediatric Tuberculosis, along with real-time disease surveillance.
Yes. Its end-to-end Lung Cancer care continuum detects lung nodules early, measures disease progression over time, and helps manage cases to improve patient outcomes.
Qure.ai provides an AI-powered care coordination suite enabling patient triage and real-time communication across Hub & Spoke hospital networks, helping clinicians deliver timely stroke interventions anywhere.
Who Qure.ai competes with, and how each one differs
Aidoc's CT-only triage platform flags a broad catalog of acute findings (PE, ICH, C-spine fracture) mainly for US hospital systems, while Qure.ai leads with chest X-ray (qXR) alongside CT and pushes further into low-resource and emerging-market deployments for TB and lung-cancer screening.
Viz.ai is built around a clinician-notification and care-coordination workflow for stroke, pulmonary embolism and aortic disease across hospital networks, whereas Qure.ai's qXR/qER products are point-of-read detection algorithms embedded directly into the radiology report.
Lunit, listed on South Korea's KOSDAQ, concentrates on cancer screening (chest X-ray, mammography) plus pathology biomarker analysis, while Qure.ai's line spans chest X-ray, head CT and ultrasound triage across a broader set of critical-care indications including TB and stroke.
Enlitic's platform focuses on standardizing and prioritizing incoming imaging data across a radiology department's worklist rather than issuing standalone diagnostic findings, contrasting with Qure.ai's FDA-cleared detection algorithms for specific pathologies.
Butterfly Network is a hardware company selling its own handheld Butterfly iQ ultrasound probe with built-in AI guidance, while Qure.ai is software-only, integrating its AI into third-party X-ray and CT scanners (and, via a 2026 Gates Foundation grant, point-of-care ultrasound).
Qure.ai is a Mumbai-based company that develops AI software for interpreting radiology scans, including chest X-rays and head CT scans.
