Qure.ai, Microsoft Partner to Expand Lung Cancer AI in U.S.

Qure.ai has partnered with Microsoft to onboard its end-to-end lung cancer detection, measurement and management suite onto Microsoft’s Precision Imaging Network. The move is positioned to streamline deployment of the company’s AI tools across U.S. hospitals and health systems, with the stated goals of accelerating early detection, reducing clinician burden and improving patient outcomes.

Lung cancer remains a significant cause of mortality. Qure.ai’s suite—designed to span incidental detection on chest X-ray through measurement on CT to ongoing management—targets key points in the clinical pathway where delays commonly occur. By using an established network like the Precision Imaging Network, Qure.ai says hospitals can access its tools with fewer integration bottlenecks, a practical advantage for health systems that must balance security, scalability and clinical validation.

What Qure.ai is bringing to the network

The suite includes modular, FDA-cleared components that can be deployed individually or together for a cohesive workflow:

• qXR-LN — incidental detection of lung nodules on chest X-ray.

• qCT-LN Quant — measurement and quantification on CT scans.

• qTrack — patient management coordination to track cases through diagnosis and care.
  Qure.ai also deploys qER for neurocritical triage; the company notes these products are part of a broader catalogue of cleared findings used across imaging workflows.

When combined, Qure.ai positions the products as creating operational, clinical and economic advantages by reducing time to diagnosis and improving coordination between radiology, pulmonology, interventional and thoracic surgery teams.

Jim Mercadante, Chief Commercial Officer at Qure.ai, states, “The Qure.ai and Microsoft collaboration brings new levels of choice and simplicity for hospitals and health systems across the USA. It will power up access to early detection, triage and tracking to improve patient care, boost survival rates and reduce overall healthcare costs. It will also bolster a growing global roster of strategic alliances between Qure.ai, academia, governments, and pharmaceutical, life science and tech companies, united in advancing the digitisation of health. Recently, via a long-term partnership with AstraZeneca and the EDISON Alliance, Qure.ai achieved a unique five million scan milestone across 20 countries, applying AI to routine chest X-rays to illustrate the role AI can play in earlier lung cancer risk identification.”

Peter Durlach, Corporate VP and Chief Strategy Officer, Microsoft Health and Life Sciences, states, “The integration of Qure.ai’s AI solutions into the Precision Imaging Network will help health systems unlock new levels of efficiency – from faster detection and diagnosis to more coordinated multidisciplinary care. This collaboration reflects Microsoft’s commitment to empowering healthcare organisations with secure, scalable cloud AI solutions that enable earlier interventions, improved patient outcomes, and sustainable transformation across the continuum of care.”

Use cases and clinician workflow impact

Operationally, the value case for hospitals is threefold:

1. Detection: An incidental lung nodule flagged on a routine chest X-ray (qXR-LN) can trigger a clearer care pathway rather than being missed until symptoms appear.

2. Quantification: Consistent CT measurements (qCT-LN Quant) reduce variability in reporting and help multidisciplinary teams compare scans over time.

3. Coordination: qTrack supports follow-up and case management so patients move from detection to diagnosis and treatment with fewer dropouts.

For radiologists and multidisciplinary teams, the promise is workflow augmentation: automated prioritization, structured measurements and a single thread for tracking. For administrators, the appeal is faster time to actionable findings and the potential to reduce downstream emergency presentations through earlier intervention.

Adoption challenges and considerations

Integrating AI into clinical practice remains an implementation challenge. Hospitals must evaluate:

• How AI outputs integrate with existing PACS, EMR and reporting systems.

• Clinical validation within local patient populations and standard operating procedures.

• Staff training and the design of human-in-the-loop workflows to avoid alert fatigue.

• Data governance, privacy and security requirements when using cloud or networked solutions.

The Precision Imaging Network onboarding is presented as a way to reduce some of these hurdles by offering a pre-integrated path to deployment; however, local validation and change management will still determine how quickly benefits materialise in day-to-day care.