Cambridge’s Qureight Raises $20M to Build a Chest Foundation Model That Compresses Drug Trial Timelines
One imaging decision that once took two weeks now takes 48 hours. That single number defines why Cambridge’s Qureight just closed a $20 million Series B — and why the company’s chest foundation model is being watched closely by every pharma sponsor running a respiratory or cardiovascular Phase 2 or 3 trial today.
Qureight, an end-to-end imaging company that provides enterprise-grade imaging and precision endpoints for clinical trials with a focus on lung and heart disease, announced it has raised $20 million in a Series B financing, led by Molten Ventures. Existing investors Hargreave Hale AIM VCT, XTX Ventures, Guinness Ventures, Meltwind, and Ascension also participated in the round. The Qureight funding round 2026 takes the company’s total capital raised past $30 million, and the money goes directly toward one technical goal: building a general-purpose 3D chest foundation model that can be retuned into disease-specific tools in one to two months rather than the roughly year it currently takes to build from zero.
What Qureight Actually Does — and Why a Single Chest Model Changes the Equation
The company operates as an imaging CRO, or contract research organisation, which means pharmaceutical companies hire it to handle the imaging side of their clinical trials — everything from setting up imaging sites around the world to collecting, managing, and analysing the scan data. That sounds like commodity work. The technical architecture underneath it is not.
Most AI imaging startups build one model per disease. Qureight is building a single model for the whole chest, then spinning the rest out of that. The distinction matters enormously for pharma sponsors. The new imaging lab is built around a general-purpose chest foundation model. Thillai says this lets Qureight develop disease-specific deep learning models in just one or two months, instead of about a year if starting from scratch. For a sponsor negotiating a timeline with regulators, that compression is not a marginal gain — it is a structural advantage in trial design.
A large share of the new capital will go toward building what Qureight calls an AI imaging laboratory, at the centre of which sits the chest foundation model — a large AI system trained on extensive datasets of chest scans. Once trained, it can be adapted to recognise patterns in different diseases. The key advantage is speed: because the core system has already learned the basic structures present in chest images, building new disease-specific tools requires less data and less time.
The platform that sits on top of this architecture has two core products. The first, called Workflow, speeds up imaging decisions, cutting turnaround time from about two weeks to 48 hours. The second, AI Lung, builds detailed models of airways and blood vessels in lung and heart scans, helping companies assess how drugs affect disease progression.
The Synthetic Control Arms Angle That Most Coverage Ignores
The Qureight AI chest imaging story gets more commercially interesting when you move past the imaging infrastructure and look at data science. The platform includes data science products — specifically synthetic control arms — that enable comparisons to real trial arms, reducing study costs and duration.
Synthetic control arms are AI-generated stand-ins for the traditional placebo group in a randomised controlled trial. They use external data sources to replace or supplement traditional control groups in clinical trials and can lower costs and accelerate timelines while ensuring robust data integrity. For a pharma sponsor, recruiting a full control group is one of the most expensive and time-consuming parts of any Phase 2 or 3 study. The benefits of using synthetic control arms for the pharmaceutical industry and patients are numerous: lower trial costs, reduced delays, and bringing therapies faster to patients.
Regulatory acceptance has been building steadily. The US Food and Drug Administration has accepted the use of external controls when justified to support regulatory decisions, including the possibility of hybrid approaches where a trial control group is augmented with external data. For Qureight, this is not a future capability — it is a live product already deployed in active trials. Few coverage pieces on the Qureight funding round 2026 have unpacked this distinction. It matters because synthetic control arms AI clinical trials cost reduction is a concrete, measurable value proposition rather than a general AI efficiency claim.
Who Validated the Platform — and Why That Matters for Competitive Positioning
Qureight currently carries out global trials with AstraZeneca, Bristol Myers Squibb, and several biotech partners. The platform is certified to ISO 13485 and ISO 27001 standards, and its customers include AstraZeneca, Bristol-Myers Squibb, and Daiichi Sankyo. Those are not pilot customers. They are names that signal enterprise-grade regulatory compliance, which is the primary procurement barrier in clinical trial services. The Qureight AstraZeneca lung fibrosis trial relationship in particular anchors the company’s core claim that its quantitative imaging analytics work at production scale across multiple geographies simultaneously.
The financing comes as pharmaceutical companies increasingly adopt artificial intelligence and quantitative imaging technologies to improve clinical trial efficiency, reduce development costs, and generate more precise measures of therapeutic effectiveness. The market Qureight is targeting confirms the opportunity is real: the global market for lung and heart clinical trials is estimated to reach $27.5 billion by 2030, representing a compound annual growth rate of 6.9% over six years.
The AI imaging CRO clinical trials update that most competitive analysis misses is this: Qureight does not compete with diagnostic software companies selling to hospitals. Its closest European rival, Oxford’s Brainomix, sells diagnostic software to hospitals. Qureight sits earlier, inside the trial itself. That earlier position means Qureight’s AI lung biomarkers and precision endpoints shape the trial protocol, not just the imaging read at the end. That distinction gives it a stickier commercial relationship with sponsors and a longer runway inside any given programme.
The Founders, the Team, and the Technical Bet
The origin story is unusual for an AI imaging firm. Two doctors, not engineers, founded Qureight in 2018. Chief executive Muhunthan Thillai is a chest physician who still sees patients, and he started the company after a CT scan he could not confidently read.Dr Muhunthan Thillai co-founded Qureight with Dr Alessandro Ruggiero after noticing that pharmaceutical companies often struggled to use imaging data effectively, which led to delays in clinical trials.
Dr Muhunthan Thillai, co-founder and CEO, is a board-certified pulmonologist who qualified from Imperial College London and previously served as Director of Interstitial Lung Diseases at Royal Papworth and Addenbrooke’s Hospitals in Cambridge. Neither founder came from a software or machine learning background. Qureight solved this by building a senior technical team, including a chief technology officer who was global head of platform at Hewlett Packard, a chief medical officer who led research at CMR Surgical, and a chief scientific officer who was a professor of machine learning at Imperial College London.
The Molten Ventures healthtech investment today carries some notable signals about investor sentiment. Qureight approached Molten two years ago and Molten said no. This time, three investors sent term sheets in six weeks. It turned down US offers to keep the round with a London fund. That dynamic — multiple competing term sheets and a deliberate preference for UK capital — says something about how the AI-in-drug-development thesis has hardened among European VCs since 2024.
The Cambridge healthtech startup raises $20m announcement came with expansion commitments that go beyond headcount. Qureight plans to double headcount to 100 by December, open a second London office next month, and get asthma, lung cancer, and pulmonary hypertension trials running before the end of the year.
Where the $20M Goes: Disease Expansion and the New AI Lab
The Qureight Series B builds on a £1.5 million seed round in 2022 and an $8.5 million Series A in April 2024. The funding will allow Qureight to bring new products to market in new disease areas where there is high demand for advanced imaging analytics to improve clinical trial design — asthma, pulmonary hypertension, bronchiectasis, and drug-induced lung toxicity — complementing existing models in lung fibrosis and expanding the company’s market reach.
The table below maps Qureight’s current disease coverage against the expansion targets and the clinical imaging challenge each area presents:
| Disease Area | Status | Core Imaging Challenge |
| Lung fibrosis | Live, production-scale | Quantifying progressive scarring in 3D CT |
| Pulmonary hypertension | Expansion target | Measuring vascular remodelling across scan timepoints |
| Bronchiectasis | Expansion target | Tracking airway widening and structural change |
| Asthma | Expansion target | Detecting air trapping and airway inflammation markers |
| Drug-induced lung toxicity | Expansion target | Differentiating treatment effect from disease progression |
The Series B proceeds are primarily dedicated to constructing an in-house specialised AI imaging laboratory housing Qureight’s proprietary 3D chest imaging foundation model. This development marks a shift from task-specific narrow AI models toward generalised spatial representations, drastically reducing both the data volume and the development time required to deploy predictive models in new therapeutic indications.
Dr Inga Deakin, Partner at Molten Ventures, who joins the board as part of the round, put it plainly: “Qureight demonstrates how AI, powered by real-world data, can accelerate discovery and transform clinical research.” CEO Thillai framed the commercial ambition in his own statement: “Expanding our 3D imaging deep learning models with our new AI laboratory will complement our existing dominance in lung fibrosis, allow us to enter new markets, and solidify our leadership position in the lung and heart imaging CRO market.”
What the Imaging CRO Model Gets Right — and Where Risks Remain
Qureight’s business model closes a genuine gap. Traditional legacy CROs manage imaging as a logistics problem. Diagnostic AI companies sell software to radiologists in clinical settings. Qureight positions itself as the layer that sits inside the trial protocol itself — providing both the global imaging CRO infrastructure and the AI analysis that generates the precision endpoints pharma sponsors need. That integrated position is commercially sticky and hard to replicate quickly.
The risk is equally clear. Whether its chest model becomes a trial standard or stays a niche tool depends on the partnerships it lands next. The company has strong logos in AstraZeneca and Bristol Myers Squibb, but it needs to broaden that roster to validate the disease-agnostic platform claim across the new indications it is entering. Disease expansion also carries technical risk: building a model for pulmonary hypertension is not the same engineering problem as adapting a foundation model for bronchiectasis, even when both sit within the same chest anatomy.
The Qureight funding round 2026 is, at its core, a bet that one general-purpose chest foundation model can do what dozens of disease-specific models currently cannot: give pharma sponsors a single, regulatory-compliant imaging partner that follows a drug programme wherever the biology leads.
If Qureight lands two or three more Phase 3 sponsor agreements before year-end, the question of whether this is a niche tool or an industry standard will largely be answered.
Life sciences professionals assessing Qureight’s platform for upcoming trials can request a product demonstration directly at qureight.com.
Frequently Asked Questions
What is Qureight and what does its AI chest imaging platform do?
Qureight is a Cambridge-based imaging contract research organisation founded in 2018. Its platform provides AI-powered quantitative imaging analytics for lung and heart clinical trials, combining global imaging CRO services, proprietary AI lung biomarkers, and data science tools — including synthetic control arms — to help pharmaceutical companies run faster, more precise drug trials.
How much did Qureight raise in its Series B and who led the round?
Qureight raised $20 million in its Series B, announced on 29 July 2026, led by Molten Ventures. Existing investors Hargreave Hale AIM VCT, XTX Ventures, Guinness Ventures, Meltwind, and Ascension also participated. The round brings total funding past $30 million, following a £1.5 million seed in 2022 and an $8.5 million Series A in April 2024.
How does Qureight’s chest foundation model differ from standard AI imaging tools?
Standard AI imaging tools are trained for one specific disease and require large datasets and roughly a year to build from scratch. Qureight’s chest foundation model is a general-purpose 3D model of the entire chest. Because it has already learned core anatomical structures, it can be adapted into a new disease-specific model in one to two months, with significantly less training data required.
Which pharma companies already use Qureight in clinical trials?
Qureight is already running global clinical trials with AstraZeneca, Bristol Myers Squibb, and Daiichi Sankyo, along with several biotech partners. The platform holds ISO 13485 and ISO 27001 certification, meeting the compliance standards required for enterprise pharmaceutical use.
What are synthetic control arms and how do they reduce trial costs?
A synthetic control arm is an AI-generated virtual comparison group that replaces or supplements the traditional placebo arm in a clinical trial. By drawing on historical patient data, it reduces the number of participants a sponsor needs to recruit, lowering costs and shortening timelines. Both the FDA and EMA have guidance frameworks supporting their use in appropriate trial designs.
How fast can Qureight build a new disease model using its foundation model?
Qureight’s CEO Dr Muhunthan Thillai says the chest foundation model allows the company to develop a new disease-specific deep learning model in one to two months. Without the foundation model, building the same tool from scratch would take approximately a year, making this speed advantage a significant differentiator for trial sponsors with tight development timelines.
What diseases is Qureight expanding into with the new funding?
Qureight currently focuses on lung fibrosis. The Series B funds expansion into asthma, pulmonary hypertension, bronchiectasis, drug-induced lung toxicity, and lung cancer — all areas with growing demand for advanced imaging analytics and limited existing tools for objective, repeatable measurement of disease progression.
Who founded Qureight and what is their background?
Qureight was co-founded in 2018 by Dr Muhunthan Thillai and Dr Alessandro Ruggiero. Thillai is a consultant chest physician at Royal Papworth Hospital in Cambridge with a PhD in Immunology and Proteomics from Imperial College London, and he continues to see patients part-time. Ruggiero brings expertise in thoracic radiology. Neither founder has an engineering background, which led them to recruit a senior technical leadership team.
