Qureight, a Cambridge-based artificial intelligence company specializing in medical imaging for clinical trials, has announced a $20 million Series B funding round led by Molten Ventures. The investment brings the company's total funding to over €27 million, marking a significant milestone in the rapidly evolving field of AI-driven drug development. The capital will be used to build a foundation model for chest imaging—a general-purpose 3D model of the entire chest cavity that can be rapidly adapted to analyze specific lung and heart diseases.
A Foundation Model Approach to Medical Imaging
Most AI imaging startups develop separate models for each disease—one for lung cancer, another for pulmonary fibrosis, a third for heart failure. Qureight is taking a different path. By creating a single, robust 3D model of the chest, the company aims to capture the full anatomical and pathological complexity of the thoracic region. From this foundational model, disease-specific tools can be spun out in just one to two months, compared to the typical one-year development cycle from scratch.
This approach leverages recent advances in foundation models and transfer learning, techniques that have revolutionized natural language processing and are now making inroads into biomedical imaging. The chest model is trained on vast datasets of CT scans, learning to recognize not only healthy tissue but also a wide range of abnormalities—from nodules and consolidations to vascular remodeling and cardiac irregularities. Once trained, it can be fine-tuned for specific clinical endpoints required in pharmaceutical trials, such as quantifying fibrosis progression or tracking changes in airway dimensions.
Accelerating Lung and Heart Clinical Trials
The core value proposition for drugmakers is speed and precision. Clinical trials for lung and heart diseases are notoriously slow and costly, partly due to the manual and often subjective interpretation of imaging endpoints. Qureight's AI tools automate and standardize this process. One of its flagship applications reduces the time needed to make imaging decisions during a trial from approximately two weeks to just 48 hours. Another tool builds detailed three-dimensional maps of airways and blood vessels from CT scans, allowing researchers to visualize and quantify how a candidate drug alters disease anatomy over time.
Perhaps most innovative is the company's synthetic control arm technology. Using historical patient data and AI-generated counterfactuals, Qureight creates virtual placebo groups that can replace or augment real control arms in clinical trials. This reduces the number of patients needed, shortens recruitment timelines, and slashes overall trial costs—sometimes by millions of dollars. Synthetic controls are gaining regulatory acceptance in certain contexts, and Qureight is positioning itself at the forefront of this trend.
Early Adoption by Major Pharma
The technology has already attracted the attention of some of the world's largest pharmaceutical companies. AstraZeneca and Bristol Myers Squibb are using Qureight's tools in ongoing lung and heart trials, according to reports. These partnerships validate the platform's utility and provide real-world feedback for further refinement. The addressable market Qureight is chasing—lung and heart clinical trials—is projected to reach $27.5 billion by 2030, a reflection of the growing burden of respiratory and cardiovascular diseases globally.
From a Doctor's Dilemma to a Tech Company
Qureight's origin story is unusual in the AI imaging space, which is often dominated by computer scientists and engineers. The company was founded in 2018 by two physicians: CEO Muhunthan Thillai, a practicing chest physician, and a co-founder who also has a medical background. The idea emerged after Thillai encountered a CT scan that he could not confidently interpret, prompting him to seek a more data-driven, quantitative approach to imaging analysis.
Recognizing the gap between clinical needs and technical capabilities, the founders assembled a seasoned leadership team. The company's senior technical bench includes a former global platform head at HP and a machine learning professor from Imperial College London. This blend of deep clinical insight and world-class AI expertise has been critical in developing tools that are both clinically relevant and technically robust.
Competitive Landscape and Positioning
Qureight operates in a growing field of AI imaging companies targeting clinical trials. Its closest European rival is Oxford's Brainomix, which sells diagnostic software to hospitals. Unlike Brainomix, Qureight positions itself earlier in the drug development pipeline—inside the clinical trial itself, rather than in post-approval clinical workflows. This distinction allows Qureight to capture value from the high-stakes, high-cost decision-making that determines whether a drug moves forward to regulatory approval.
Other competitors include companies like Imbio (which focuses on lung perfusion) and AI startups embedded within large pharmaceutical firms. However, Qureight's foundation model strategy could give it a differentiation edge: the ability to rapidly pivot to new disease areas without starting from scratch, and the potential to offer a unified imaging platform across multiple therapeutic areas within the chest.
Funding Journey and Investor Sentiment
The Series B round also highlights a shift in investor appetite for AI-in-medicine. Qureight initially approached Molten Ventures two years ago and was turned down. This time, the process was dramatically different—three investors sent term sheets within six weeks. The company received offers from US-based funds but chose to keep the round with a London-based venture capital firm, Molten Ventures, to maintain a European home base.
This surge of interest mirrors a broader trend: capital is flowing heavily into AI for drug discovery and biology. In 2024 and 2025, health-tech and AI-biotech raises have proliferated, with investors betting that machine learning can significantly reduce the time and cost of bringing new therapies to market. Qureight's focus on clinical trial imaging—a critical bottleneck in drug development—resonates with this thesis.
Expansion Plans and Outlook
With the new funding, Qureight plans to double its headcount to 100 employees by the end of the year. It will ramp up efforts to develop disease-specific models for asthma, lung cancer, pulmonary hypertension, and bronchiectasis, in addition to its existing work in idiopathic pulmonary fibrosis. The company also aims to deepen its partnerships with pharmaceutical companies and explore regulatory pathways that could allow its AI outputs to be used as primary or secondary endpoints in registration trials.
Whether Qureight's chest foundation model becomes an industry standard or remains a niche tool will depend heavily on the partnerships it secures next. Key factors include the ability to demonstrate robust performance across diverse patient populations, to integrate seamlessly into pharma workflows, and to maintain data security and compliance with evolving regulations such as the EU AI Act and FDA guidance on AI/ML-enabled medical devices.
The broader context is promising. Lung diseases, including chronic obstructive pulmonary disease, lung cancer, and fibrosis, are among the leading causes of death worldwide. Heart failure and pulmonary hypertension similarly affect millions. Clinical trials for these conditions often require expensive imaging-based endpoints, and any technology that speeds up or reduces the cost of these trials could have a massive public health impact.
As Qureight scales, it will likely face challenges common to AI startups in regulated industries: proving clinical validity, managing expectations, and navigating the complexities of global clinical trial operations. The company's physician-led founding gives it credibility with clinical audiences, while its strong technical hires provide the necessary engineering depth.
In the near term, the company will focus on product development and customer acquisition. Its platform is already generating revenue through partnerships with large pharma, and the Series B funding provides runway for several years of operation. If the foundation model delivers on its promise, Qureight could become an essential partner for any company developing drugs for lung or heart disease, transforming how imaging data is used to make go/no-go decisions in drug development.