Summary

• Cambridge-based Qureight has raised $20m in Series B funding to expand AI imaging for clinical trials.
• The company plans to build an AI imaging laboratory and extend its 3D chest imaging models into new disease areas.
• The story sits in clinical trial infrastructure, pharmaceutical R&D productivity, and health data implementation.

Qureight has raised $20m in Series B funding to expand its AI imaging platform for clinical trials, adding capital to a part of health technology focused on evidence generation rather than front line diagnosis.

The Cambridge company works on AI based analysis of medical imaging, particularly in lung and heart disease. The funding will support an AI imaging laboratory, expansion of its 3D deep learning portfolio, and new disease models for areas including asthma, pulmonary hypertension, bronchiectasis, and drug induced lung toxicity.

Clinical trials often depend on complex imaging data that can be slow, inconsistent, and difficult to compare across sites. Pharmaceutical companies need reliable endpoints and biomarkers to understand whether a drug is working, yet imaging analysis can become a bottleneck when scans are generated in different settings, annotated unevenly, or interpreted with variation between readers.

AI imaging companies are often discussed through diagnosis, but Qureight’s model sits closer to trial infrastructure. Its technology is intended to help sponsors use imaging data more effectively in study design, patient selection, quantitative assessment, and disease progression analysis. In drug development, better imaging analytics can affect trial duration, cost, evidence quality, and the confidence with which companies decide whether to continue or abandon a programme.

The company’s planned AI imaging laboratory points to a wider pattern in health AI. Building useful models in medicine requires more than applying generic machine learning to scan data. It requires curated datasets, clinical expertise, disease specific validation, regulatory awareness, and systems that can be trusted by pharmaceutical sponsors, investigators, and regulators. The cost of producing that infrastructure is one reason healthcare AI can move more slowly than consumer or office productivity applications.

For the UK, the round reinforces Cambridge’s position as a cluster for AI, life sciences, and translational health technology. The country has a strong research base and a large public health system, but commercial health AI still faces hurdles around procurement, evidence standards, information governance, and adoption. Companies focused on clinical trials may sometimes avoid the slowest NHS deployment pathways by selling into pharmaceutical R&D, but they still need to meet high standards for data quality, validation, and explainability.

That distinction gives Qureight a clearer business route than many digital health companies. Pharma and biotech customers are accustomed to paying for specialist tools that improve trial performance, especially where the cost of failure is high. A platform that can reduce data requirements, speed up model development, and support better imaging endpoints has a direct link to R&D economics.

The competitive landscape remains demanding. Medical imaging AI is crowded, and customers will need proof that models generalise across scanners, sites, populations, and disease states. Clinical evidence must be strong enough to support decisions that can affect trial design and capital allocation. Qureight’s expansion into new disease areas will therefore be judged on validation as much as technical capability.

AI in healthcare is dividing into several implementation markets. Some companies automate administration, some support diagnostics, some help clinicians manage workflow, and others work upstream in research and development. Qureight sits in the last category, where AI can reshape how evidence is generated before a medicine reaches routine care.