Research Note: Quibim
Executive Summary
Quibim is a pioneering global healthcare AI company that develops and deploys advanced imaging solutions for early detection and management of critical medical conditions through deep learning algorithms and radiomics technology. The company has developed a comprehensive suite of AI-powered products, including QP-Prostate for prostate cancer detection, QP-Brain for neurological disease assessment, QP-Liver for diffuse liver disease diagnosis, and QP-Insights for multi-omics data management, all designed to transform complex medical imaging data into actionable clinical insights. Quibim has secured multiple regulatory approvals, including 13 FDA clearances and 62 CE markings, validating their technological approach while establishing their credibility in both clinical and research settings across global markets. The company, headquartered in Valencia, Spain with additional offices in New York, London, and Dubai, recently secured a significant $50 million Series A funding round in January 2025, led by Asabys and Buenavista, with participation from strategic investors including Amadeus Capital Partners, bringing their total funding to approximately $123 million since founding. Quibim's technology has already impacted over 25 million lives across 90 countries through deployments at more than 2,700 sites worldwide, demonstrating strong market validation and positioning the company as a leader in the rapidly expanding healthcare AI market that is projected to grow at a CAGR of 34-38% through 2030.
Source: Fourester Research
Corporate Overview
Quibim was founded in 2016 as a breakthrough artificial intelligence solution provider focused on disrupting the traditional radiology paradigm by enhancing medical imaging accuracy and improving health outcomes through advanced quantitative analysis. The company maintains its headquarters at Quibim Technologies Private Limited, 6th Floor, Wing E, Times Square, Andheri-Kurla Road, Marol, Andheri (East), Mumbai - 400059, Maharashtra, India, while operating international offices in New York, London, and Dubai to support their global operations and expansion efforts. The company's mission centers on transforming imaging into a catalyst for precision health, using artificial intelligence to make healthcare more accessible and affordable worldwide by addressing critical gaps in medical imaging interpretation that impact patient outcomes in both developed and developing markets. Quibim's name itself is an acronym for "Quantitative Imaging Biomarkers In Medicine," reflecting their core focus on extracting meaningful, quantifiable data from medical images to drive more precise clinical decision-making and research applications.
Quibim has built a strong leadership team with co-founder and CEO Ángel Alberich-Bayarri bringing significant expertise in AI and data science to the organization, while co-founder Dr. Luis Martí-Bonmatí provides deep radiological expertise as an experienced medical professional. The corporate structure includes dedicated departments for research and development, regulatory affairs, commercial operations, and clinical partnerships, with Chief Regulatory Affairs Officer Bunty Kundnani playing a key role in the company's successful regulatory strategy that has yielded multiple FDA and CE approvals. The company has attracted significant venture capital backing, most recently completing a $50 million Series A funding round in January 2025, led by Asabys and Buenavista Equity Partners, with participation from the Merck Global Health Innovation Fund, UI Investissements, GoHub Ventures, and existing investors including Amadeus Capital Partners, APEX Ventures, Partech, Adara Ventures, and Leadwind. The company operates with approximately 276 employees across its global offices, with teams focused on AI research, product development, clinical validation, commercial operations, and strategic partnerships.
Source: Fourester Research
Market Analysis
The global healthcare AI market that Quibim operates within is experiencing rapid growth, with the AI in medical imaging segment showing particularly strong momentum as healthcare systems worldwide seek to enhance diagnostic accuracy and efficiency. According to market research, the global AI in medical imaging market was valued at $1.01 billion in 2023 and is projected to reach approximately $17.9 billion by 2030, growing at a compound annual growth rate (CAGR) of 34.8-37.8% during the forecast period. This substantial growth is being driven by increasing radiologist shortages, growing imaging volumes, the need for enhanced diagnostic accuracy, and accelerating adoption of AI technologies across healthcare systems globally. The U.S. market specifically was valued at $395 million in 2023 and is projected to grow at a CAGR of 33.2% through 2030, presenting significant opportunities for companies like Quibim with FDA-cleared solutions that can address these market needs.
Quibim has established a significant market presence with deployments across more than 90 countries and 2,700 sites, demonstrating global reach that extends across both developed and developing markets. The company appears particularly focused on addressing high-burden medical conditions with significant global impact, including prostate cancer, neurological disorders such as Alzheimer's, and liver diseases, positioning its solutions at the intersection of critical healthcare needs and advanced technology. The investment in Quibim's recent Series A round by pharmaceutical-related venture funds such as the Merck Global Health Innovation Fund underscores the company's potential to bridge the gap between medical imaging, pharmaceutical research, and clinical applications. The competitive landscape includes other healthcare AI companies focused on medical imaging, such as Aidoc, Viz.ai, Quibim, and EnvisionIt Deep AI, though Quibim appears to have established a differentiated position through its comprehensive regulatory approvals, diverse product portfolio, and global deployment scale.
Major pharmaceutical and medical technology companies are increasingly entering this space through partnerships and investments, as evidenced by Philips' integration of Quibim's AI models into their MRI scanners, and strategic collaborations with organizations like Johnson & Johnson's MedTech division, AstraZeneca, and various healthcare systems worldwide. These partnerships have helped Quibim reach significant scale, with their solutions having impacted over 25 million lives globally according to company statements. The market is expected to continue expanding as healthcare systems seek to address workforce shortages, improve diagnostic accuracy, and accelerate treatment pathways, creating favorable conditions for Quibim's ongoing growth across both developed and emerging markets. With a strong focus on creating AI solutions that enhance rather than replace radiologist expertise, Quibim is well-positioned to capitalize on the increasing demand for intelligent tools that augment clinical workflows and decision-making processes.
Product Analysis
Quibim's product portfolio is built around a core AI platform that uses deep learning technology and radiomics to provide automated interpretation of radiology exams across multiple modalities, improving diagnostic accuracy and efficiency for healthcare providers worldwide. The company has developed multiple specialized products, with their flagship solutions including QP-Prostate, QP-Brain, QP-Liver, and QP-Insights, each designed to address specific clinical needs while maintaining a unified technological approach to medical image analysis. These products leverage Quibim's expertise in quantitative imaging biomarkers to extract actionable insights from medical images that might otherwise be overlooked in traditional radiological interpretation. The QP-Prostate solution, Quibim's flagship product, has received FDA 510(k) clearance and is designed to enhance prostate cancer detection through automated prostate segmentation and lesion detection capabilities. It provides precise quantitative information and automates tasks like PI-RADS compliance checks, resulting in improved and standardized decision-making for prostate cancer diagnosis.
QP-Brain is an AI-based software designed for early-stage neurological disease quantification, providing tools for precise brain volumetry and white matter hyperintensity (WMH) analysis. The brain volumetry tool delivers accurate volume and morphometry calculations to support disease classification and progression of conditions like Alzheimer's, multiple sclerosis, and vascular and frontotemporal dementia. QP-Liver is a post-processing solution that quantifies fat and iron concentration to provide a comprehensive evaluation of steatosis and iron overload, automating the segmentation of the whole abdomen and liver to significantly reduce the time clinicians spend on manual segmentation and enhance efficiency in clinical settings. QP-Insights serves as a comprehensive web-based cloud platform that manages, stores, and analyzes large-scale multi-omics data and medical images for clinical studies and research projects, supporting both healthcare providers and pharmaceutical companies in managing and extracting value from complex imaging datasets.
These four core products are complemented by additional specialized solutions for various clinical applications, creating a comprehensive ecosystem for medical imaging analysis across different body systems and modalities. Quibim's products are designed to integrate seamlessly with existing healthcare workflows and systems, offering deployment flexibility through cloud-based or on-premises options, with support for both web-based interfaces and dedicated gateway software. The company's regulatory clearances and certifications, including multiple FDA clearances and CE markings, provide healthcare organizations with confidence in the safety and effectiveness of these AI solutions when incorporated into clinical workflows and decision-making processes. Quibim's product roadmap appears focused on expanding both their regulatory approvals in additional markets and developing new applications for their core AI technology, with reports indicating plans for products targeting breast and lung imaging analysis in the near future.
Technical Architecture
Quibim's technical architecture is built around sophisticated deep learning algorithms and radiomics technology that form the foundation of their medical imaging analysis capabilities, with their core technology using artificial intelligence to classify radiology images, diagnose disease, and highlight abnormalities that might otherwise be overlooked by human readers. The company's AI models are developed using large-scale training datasets of medical images, with published research indicating their systems have been trained and validated on millions of radiological images to ensure accuracy and reliability across diverse patient populations and equipment types. For their head CT algorithms, for example, Quibim has utilized over 300,000 images of head CT scans plus their medical reports from multiple centers, with more than 21,000 scans held out to validate the final model, demonstrating the company's commitment to rigorous validation methodologies that support their regulatory clearances and clinical implementations.
The technical implementation of Quibim's solutions includes both cloud-based and on-premises deployment options, with solutions like QP-Insights described as a web-based cloud platform that supports multi-modality imaging analysis on mobile and desktop devices. For environments requiring local processing, the company also offers on-premises deployment specifications to accommodate healthcare organizations with specific data residency or security requirements. Quibim appears to take a methodical approach to addressing common challenges in developing AI for medical imaging, including class imbalance in training data and the three-dimensional nature of CT scans, with techniques inspired by other domains like video recognition to handle complex spatial data. The company's engineers have developed advanced image quality harmonization techniques that minimize variability across different equipment vendors and acquisition protocols, ensuring consistent performance regardless of the imaging equipment being used.
Integration capabilities appear to be a key architectural consideration, with the platform offering API specifications that allow for connectivity with existing healthcare IT ecosystems, PACS systems, and electronic health records. Security features are embedded throughout the architecture, with compliance for healthcare-specific regulations like HIPAA and GDPR explicitly mentioned in their application descriptions, which is essential for handling sensitive patient data in clinical settings. For organizations requiring documentation and transparency in AI decision-making, Quibim's technical architecture provides output specifications that explain how the algorithms arrive at their conclusions, addressing an important consideration for clinical adoption and regulatory compliance. The platform's architecture includes real-time image processing capabilities, with their application allowing clinicians to view results immediately, facilitating rapid clinical decisions particularly in time-sensitive scenarios like stroke or trauma cases where minutes can make a significant difference in patient outcomes.
Strengths
Quibim demonstrates remarkable regulatory achievement with 13 FDA clearances and 62 CE marking approvals, establishing strong credibility and compliance validation that differentiates them in the competitive healthcare AI market. This regulatory success spans multiple imaging modalities and clinical applications, giving the company a significant competitive advantage when engaging with healthcare systems that require robust regulatory validation before adopting new technologies. Their technical capabilities have been validated through multiple peer-reviewed studies and clinical implementations, with published research showing high accuracy rates for their algorithms, including a high negative predictive value of 99.8% for detecting lung nodules and an impressive 91.5% AUC (Area Under Curve) score for identifying lung nodules. These performance metrics demonstrate the company's technical excellence in developing AI models that can achieve clinically relevant accuracy and reliability, essential for gaining trust from healthcare professionals in real-world clinical settings.
The company has established strategic partnerships with major healthcare and pharmaceutical organizations, including Johnson & Johnson MedTech, AstraZeneca, Philips, NHS Greater Glasgow and Clyde, and Harvard-affiliated Mass General Brigham healthcare system, demonstrating the ability to integrate with established healthcare ecosystems and gain institutional trust. These partnerships not only provide validation of Quibim's technology but also create pathways for widespread adoption across healthcare systems and markets. Their global deployment scale is impressive, with solutions implemented across 90 countries and 2,700 sites, impacting over 25 million lives, which provides both market validation and valuable real-world performance data to continuously improve their algorithms. This global footprint demonstrates the company's ability to adapt its technology to diverse healthcare environments and requirements, from advanced hospital systems to resource-constrained settings.
Quibim has demonstrated particular strength in addressing global health challenges through its specialized solutions for conditions with high clinical needs, with specific products showing measurable impact on diagnostic accuracy and workflow efficiency. The diversity of their product portfolio, spanning prostate, brain, liver, and multi-omics data management, allows them to address multiple clinical needs with a unified technological approach, creating a comprehensive ecosystem for medical imaging analysis. The company's AI solutions have shown tangible clinical benefits, with implementations demonstrating accelerated diagnostic timelines, improved detection rates, and enhanced workflow efficiency for radiologists and clinicians. Their technology is designed to work with existing hardware infrastructure, including integration with various imaging systems and PACS solutions, making deployment feasible across a wide range of healthcare environments—a significant advantage for global market penetration and adoption by healthcare systems with varying levels of technological sophistication.
Weaknesses
While Quibim has secured impressive regulatory approvals, their development as a relatively young company (founded in 2016) means they may face challenges competing against larger, more established healthcare technology providers with broader product portfolios and deeper institutional relationships. This relative youth may present challenges in terms of organizational maturity, process refinement, and the depth of customer relationships needed to drive large-scale enterprise adoption in conservative healthcare environments that typically move slowly when adopting new technologies. Despite their global presence, the company's headquarters in Valencia, Spain may present perception challenges in some Western healthcare markets where decision-makers might prefer vendors with primary operations in North America or Europe, though they have established offices in New York, London, and Dubai to address this potential limitation. Cultural differences and time zone challenges could still impact customer support and relationship management in certain markets, requiring additional resources and attention to maintain service quality as they expand globally.
The company's focus on specific imaging modalities and medical conditions, while allowing for specialization, may limit their total addressable market compared to healthcare AI companies with broader clinical applications beyond radiology. Their heavy emphasis on conditions like prostate cancer and neurological disorders, while addressing important clinical needs, may not align perfectly with the top priorities of some healthcare systems with different disease burden profiles, potentially requiring market-specific product adaptation strategies. The deployment of AI in clinical settings often requires significant change management and workflow integration, which may present implementation challenges despite the technical strength of Quibim's solutions. Healthcare organizations frequently struggle with integrating new technologies into established workflows, particularly when clinical decision-making processes and responsibilities may be affected, requiring Quibim to invest in customer success and implementation support resources.
While the company has secured $123 million in funding, this is still relatively modest compared to some competitors in the broader healthcare AI space, potentially limiting their ability to rapidly scale commercial operations, marketing, and customer support infrastructure to match their global deployment ambitions. Public information about Quibim's comprehensive performance metrics across different clinical settings, equipment types, and patient populations remains somewhat limited, which may present challenges for healthcare organizations conducting thorough vendor evaluations. The rapidly evolving regulatory landscape for AI in healthcare could present ongoing compliance challenges, particularly regarding continuously learning algorithms, requiring significant investment in regulatory affairs to maintain their current approval momentum and adapt to new regulatory frameworks being developed by agencies like the FDA and EU regulatory bodies. As healthcare AI regulations continue to evolve, particularly for adaptive algorithms, Quibim will need to navigate complex and potentially changing requirements across multiple jurisdictions.
Client Voice
Healthcare providers implementing Quibim's solutions have reported significant operational benefits, with institutions like NHS Greater Glasgow and Clyde noting that their tools help them swiftly identify critical cases, enabling them to prioritize and provide timely care to patients who require it most. These benefits directly translate to improved clinical workflow efficiency and potentially better patient outcomes through faster diagnosis and treatment initiation. A UK physician working with Quibim's chest X-ray solution remarked on its ability to prioritize potential lung cancer patients for fast-tracked CT scans, highlighting the technology's role in accelerating critical diagnostic pathways for high-risk patients. Customers particularly value Quibim's compatibility with existing infrastructure, with one client noting that their AI technology works with all kinds of machines, both new and old, ensuring that healthcare organizations can leverage their existing hardware investments rather than requiring expensive equipment upgrades.
Implementation experiences appear positive based on available testimonials, with clients reporting relatively straightforward integration processes and emphasizing the platform's ability to screen patients promptly, obtain immediate results, and quickly refer patients for further investigation when needed. This streamlined workflow is crucial for healthcare organizations dealing with high imaging volumes and limited specialist resources. Specific outcome metrics have been shared by some clients, including an evaluation in India showing that Quibim's QP-xR solution improves efficiency while reducing costs compared to existing clinical pathways, particularly in high tuberculosis burden regions. Another implementation with the KwaZulu-Natal Department of Health collaboration demonstrated identification of 187 TB cases in six months that might have been missed through conventional approaches, showcasing the real-world clinical impact of Quibim's technology in resource-constrained settings.
Clinical validation studies conducted with partners have yielded compelling results, including a study at Phrapokklao Cancer Centre that evaluated newly diagnosed lung cancer patients and found that Quibim's technology could detect signs of lung cancer in chest X-rays taken up to three years before clinical diagnosis. This capability for earlier detection represents a potentially transformative impact on cancer diagnosis and treatment planning. Healthcare facilities also report operational improvements, with implementation at North East London Cancer Alliance aimed at optimizing radiology workflow and diagnostic lung cancer pathways with AI, specifically noting that identifying suspicious signs of lung cancer such as nodules or masses on chest X-rays helps move patients into the next step of the diagnostic journey quicker. These testimonials collectively demonstrate the practical value that Quibim's solutions deliver across diverse healthcare settings, from emergency departments and radiology departments to public health screening programs in both developed and resource-constrained environments.
Bottom Line
Quibim represents a compelling investment in healthcare AI for organizations seeking to enhance diagnostic speed, accuracy, and clinical workflow efficiency, particularly in radiology departments dealing with high imaging volumes and workforce constraints. The company's extensive regulatory approvals (13 FDA clearances and 62 CE markings) provide essential validation for healthcare organizations requiring compliant AI solutions, while their global deployment scale across 90 countries offers confidence in the platform's adaptability to diverse healthcare environments. Organizations struggling with radiologist shortages, lengthy imaging interpretation turnaround times, or challenges in prioritizing urgent cases among high volumes of routine studies would benefit most from Quibim's solutions, as would healthcare systems focused on specific conditions like prostate cancer, neurological disorders, and liver diseases where their specialized solutions have demonstrated clinical value. Healthcare systems in both developed and developing markets can leverage Quibim's technology to address different challenges – from improving operational efficiency and diagnostic accuracy in resource-rich environments to extending radiology capabilities in settings with limited specialist access.
Implementation considerations should include integration with existing PACS and electronic health record systems, workflow adaptation to incorporate AI findings into clinical practice, and change management to ensure clinician adoption and trust in the AI recommendations. For successful deployment, organizations should plan for appropriate IT infrastructure, consider both cloud and on-premises options based on their security and data residency requirements, and establish clear protocols for how AI findings will be incorporated into clinical decision-making processes. When evaluating potential return on investment, healthcare organizations should consider not only efficiency gains and potential cost savings from faster diagnosis and treatment but also the value of potentially detecting conditions earlier, reducing diagnostic errors, and optimizing specialist resources for the most complex cases. Organizations should establish clear success metrics before implementation, focusing on both operational improvements (such as reduced report turnaround times) and clinical outcomes (such as earlier detection rates).
Given Quibim's strong market position, extensive regulatory approvals, significant funding, and demonstrated clinical impact, they represent a leading option for healthcare organizations looking to strategically implement AI in radiology and should be shortlisted for serious consideration by institutions ready to incorporate advanced imaging AI into their clinical practice. For maximum value, healthcare organizations should consider implementing Quibim's solutions as part of a broader strategy for enhancing radiology services and diagnostic pathways, ensuring that the AI technology complements and enhances clinical expertise rather than attempting to replace it. Appropriate governance and quality assurance processes should be established to monitor performance and impact over time, with regular evaluation against both operational and clinical metrics to ensure the technology continues to deliver on its promised benefits. Quibim's commitment to creating digital twins of organs and eventually the entire human body positions them as a forward-thinking partner for healthcare organizations with long-term visions for incorporating advanced AI into their clinical and research programs.
Strategic Planning Assumptions
Because Quibim has demonstrated impressive regulatory success with 13 FDA clearances and 62 CE markings across multiple imaging modalities, by 2027, they will secure approval for at least 10 additional AI applications, expanding their addressable market by 40% beyond their current radiology focus while navigating increasingly complex regulatory frameworks for adaptive AI algorithms (Probability: 0.85).
Because of Quibim's strategic partnership with Philips to integrate their AI models into MRI scanners and its recently secured $50 million in Series A funding, by 2026, the company will establish partnerships with at least three additional major medical device manufacturers, significantly expanding their distribution channels and market penetration across both developed and emerging markets (Probability: 0.80).
Because healthcare systems worldwide face persistent radiologist shortages while imaging volumes continue to increase at 15% annually, by 2028, AI-assisted radiology workflows like those offered by Quibim will be implemented in more than 60% of hospitals with over 250 beds, delivering documented time savings of 25-40% in routine interpretation tasks (Probability: 0.85).
Because Quibim's platform has demonstrated the ability to extract valuable insights from medical images that often go undetected in standard radiological readings, by 2027, at least 30% of pharmaceutical companies involved in oncology drug development will incorporate Quibim's AI imaging analysis into their clinical trials to identify responders and non-responders more accurately (Probability: 0.75).
Because Quibim's technology has shown potential for detecting disease signs years before clinical diagnosis, as demonstrated in their lung cancer studies, by 2028, healthcare payors will begin offering reimbursement incentives for preventive AI imaging analysis, recognizing the long-term cost savings from earlier intervention and treatment (Probability: 0.65).
Because the healthcare AI market is consolidating with larger healthcare technology companies acquiring specialized AI firms to complement their existing product portfolios, by 2027, Quibim will either be acquired by a major healthcare technology company for more than $500 million or achieve unicorn status with a valuation exceeding $1 billion through continued independent growth (Probability: 0.75).
Because Quibim has established a strong foundation in organ-specific AI analysis (prostate, brain, liver), by 2028, they will successfully launch their full-body digital twin technology, enabling comprehensive health assessment and monitoring through integrated multi-organ AI analysis in a single imaging session (Probability: 0.70).
Because healthcare reimbursement models are increasingly shifting toward value-based care that rewards earlier intervention and improved outcomes, by 2026, at least 15 major insurers will offer specific reimbursement for AI-assisted diagnoses like those provided by Quibim, recognizing their contribution to earlier detection and treatment that reduces overall care costs (Probability: 0.60).
Because Quibim has successfully deployed their technology in diverse healthcare environments across 90 countries, by 2027, they will establish regional AI development centers in at least three additional countries to ensure their algorithms address local disease patterns and patient demographics, while adapting to country-specific regulatory requirements (Probability: 0.75).
Because generative AI is rapidly transforming healthcare applications and Quibim has demonstrated technical leadership with ongoing algorithm innovation, by 2026, Quibim will release next-generation products that combine their existing deep learning capabilities with generative AI to provide not just detection but also detailed clinical recommendations and treatment pathway suggestions, requiring new approaches to regulatory validation and clinical trust (Probability: 0.70).