Quibim, a medical imaging AI specialist, launched QP-Breast in Europe and the United Kingdom on July 2, 2026, delivering a regulated artificial intelligence tool that detects suspicious lesions on breast MRI scans and generates cancer probability maps. The software has received both CE and UKCA marks, making it one of the first dedicated breast MRI AI solutions to gain dual regulatory clearance in the region. Radiologists can now use QP-Breast as an adjunctive aid in their workflow, with the software designed to highlight regions of interest and assign a malignancy risk score, helping to prioritise cases and reduce interpretation time.
Breast MRI is the most sensitive imaging modality for detecting breast cancer, especially in dense tissue and high-risk populations. Yet it remains notoriously challenging to read. Subtle enhancements, complex background parenchymal enhancement, and the sheer volume of images in a multiparametric study can overwhelm even experienced radiologists. Current clinical practice relies heavily on manual analysis, which introduces variability and can lead to missed cancers or unnecessary biopsies. QP-Breast addresses these pain points by providing a consistent, objective second read that quantifies the probability of malignancy for every enhancing lesion.
At its core, QP-Breast applies deep learning algorithms trained on a diverse dataset of breast MRI examinations with histopathological ground truth. When a study is sent to the software, it automatically segments breast tissue, extracts kinetic and morphological features, and produces a colour-coded overlay map. Red zones indicate a high likelihood of cancer, while green denotes benign or likely benign findings. A numerical score between 0 and 100 is assigned to each detected lesion, giving clinicians a straightforward metric to inform biopsy decisions or short-term follow-up. Because the output follows the Breast Imaging Reporting and Data System (BI-RADS) lexicon, it fits naturally into existing reporting templates and multidisciplinary meetings.
The significance of the CE and UKCA marks cannot be overstated. Under the European Medical Device Regulation (MDR) 2017/745, CE marking confirms that QP-Breast meets strict safety, performance, and quality requirements. The UKCA mark, obtained from the UK’s Medicines and Healthcare products Regulatory Agency (MHRA), extends that assurance to Great Britain. Together, these certifications allow Quibim to market the product throughout the European Economic Area, Switzerland, and the UK—regions that collectively perform millions of breast MRI exams each year. For hospital procurement teams, the dual marks reduce legal and clinical governance burdens, because QP-Breast can be deployed under existing quality management systems without additional local validation studies for regulatory compliance.
From a technical standpoint, QP-Breast integrates with standard DICOM workflows. Hospitals can install the software on an on-premises Windows server or a virtual machine within their existing radiology IT environment. The engine communicates with picture archiving and communication systems (PACS) and modality worklists, automatically fetching relevant prior studies for comparison if available. Results are then pushed back as secondary capture DICOM objects or structured reports, viewable alongside the source images on any certified workstation. The Windows-based deployment model is a deliberate choice: most hospital imaging informatics run on Windows Server, and Radiologists’ reporting stations are overwhelmingly Windows 10 or 11 machines. This ensures that QP-Breast slots into the clinical workflow without requiring parallel infrastructure, a critical factor for time-pressed departments.
For radiologists, the tool promises to cut reading time significantly. A typical high-risk screening or diagnostic MRI generates hundreds of dynamic images. Instead of scrolling through each series to track enhancement curves, the reader can immediately focus on the heatmap and cross-reference the most suspicious areas. In silico validation presented by Quibim suggests that QP-Breast achieves an area under the receiver operating characteristic curve (AUC) in excess of 0.90 for malignancy detection, with sensitivity above 95% at a specificity level that can halve false-positive recommendations. These performance figures, while pending independent peer-reviewed publication, indicate that the AI can act as a reliable safety net—catching lesions the human eye might dismiss while also reducing the number of harmless findings flagged for biopsy.
Patient benefits flow from this dual gain in accuracy and efficiency. Faster reporting means women receive results sooner, shrinking the anxiety-laden waiting period that follows a suspicious mammogram or clinical finding. More accurate triage also means fewer callbacks for benign enhancements, which often lead to unnecessary MRI-guided biopsies—a procedure that is invasive, costly, and emotionally draining. In screening programmes for BRCA mutation carriers or other high-risk groups, QP-Breast could become a standard component of the reading protocol, analogous to how computer-aided detection shaped mammography two decades ago but with far richer phenotypic information.
Quibim, headquartered in Valencia, Spain, has built its reputation on AI-powered imaging biomarkers. The company’s platform already includes tools for prostate, lung, and liver MRI analysis. QP-Breast completes the oncology portfolio and leverages the same regulatory framework and technical backbone. The launch aligns with a broader industry shift toward regulated “Software as a Medical Device” (SaMD), where AI modules must prove not only clinical validity but also continuous performance monitoring post-deployment. Quibim has indicated that QP-Breast’s algorithms will be periodically retrained on updated datasets, with version-controlled releases that maintain the CE/UKCA certification through notified-body oversight.
The competitive landscape for breast MRI AI is heating up. Several companies have received FDA clearance in the United States, but the European market has been slower to adopt regulated solutions. Quibim’s first-mover advantage with dual CE and UKCA marks positions it strongly in public health systems such as the NHS and the Spanish National Health System, where regulatory compliance is a prerequisite for large-scale procurement. By focusing on MRI rather than mammography or ultrasound, Quibim targets a niche where AI adds immediate clinical value, as MRI interpretation complexity is widely acknowledged.
That said, any AI tool raises important questions about transparency, accountability, and the risk of automation bias. Quibim addresses these by displaying confidence scores alongside each finding, allowing the radiologist to overrule or confirm the AI’s suggestion. The software logs every interaction, creating an audit trail that supports medico-legal defensibility. Furthermore, because the CE/UKCA process requires clinical evidence demonstrating a favourable benefit-risk balance, healthcare providers can be assured that QP-Breast has been scrutinised under the same rigorous standards as an MRI contrast injector or a biopsy needle.
Looking ahead, Quibim plans to extend QP-Breast’s capabilities to other MRI manufacturers and protocols, including abbreviated breast MRI protocols that are gaining traction for population-wide screening in women with dense breasts. The firm is also working on integrating the tool with genomic and liquid biopsy data, painting a future where imaging AI contributes to truly personalised risk assessment. For now, the immediate impact for Windows-powered radiology departments is clear: a clinically validated, regulation-ready AI assistant that can be installed alongside existing PACS software, requiring minimal training yet promising a measurable upgrade in diagnostic confidence.
As European and UK healthcare systems grapple with radiologist shortages and mounting backlogs, tools like QP-Breast may help bridge the gap. By automating the most time-consuming steps of lesion detection and characterisation, the AI can free up specialists to focus on complex cases and patient communication. The July 2026 launch therefore represents not just a product release but a tangible step toward embedded AI in everyday radiology—one that honours the stringency of medical device regulation while offering pragmatic utility on the ground.
Quibim has not disclosed pricing, though early interest from NHS trusts and private hospital groups suggests a subscription or per-study licensing model aligned with volume. More details will emerge when the software is exhibited at the upcoming European Congress of Radiology. In the interim, radiologists and IT managers interested in evaluating QP-Breast should follow Quibim’s official channels for a white paper detailing the clinical validation study and integration specifications.