Unlocking the Power of Imaging: How Yunu is Revolutionizing Clinical Trials with AI

In this episode of Technology in Science, Matthew Witchell speaks with Jeffrey Sorenson, CEO of Yunu, to explore how AI-powered imaging is transforming the way clinical trials are designed, measured, and accelerated. From oncology to precision medicine, Yunu’s stateful imaging data network is redefining how pharmaceutical, biotech, and medical device companies collect, manage, and apply clinical data management.

The conversation highlights how imaging data is becoming a foundation for discovery. By connecting sites, CROs, and sponsors in a single workflow, Yunu is helping the life sciences industry build faster, more accurate, and more data-rich clinical trials.

Jeffrey Sorenson, CEO of Yunu

Jeffrey Sorenson is a 30-year health-tech entrepreneur and engineer whose career spans imaging, informatics, and AI in clinical trials. As the former CEO of TeraRecon, he helped transform advanced radiology visualization before co-founding Yunu to address one of the industry’s biggest challenges: the disconnection between imaging data, labels, and outcomes.

Today, Yunu manages more than 4,000 imaging clinical trials globally, helping sponsors harness imaging data for more precise and efficient drug development.

Key Takeaways

• Imaging data is critical for precision medicine, acting as a surrogate marker for clinical trial endpoints.
• AI in clinical trials relies on well-labeled, high-quality imaging data, yet most sponsors still lack direct access to it.
• Yunu’s stateful imaging data network connects sites, CROs, and sponsors in one unified workflow, improving accuracy and speed.
• Automating imaging workflows can reduce error rates, improve patient enrollment, and accelerate drug development.
• Demand for talent in clinical data management, AI in healthcare jobs, and medical device recruitment continues to rise as innovation scales.

Imaging as the Foundation for Precision Medicine

In oncology and other high-stakes therapeutic areas, imaging plays a vital role in assessing treatment efficacy. Ninety percent of cancers have measurable imaging endpoints, and progression-free survival often determines whether a therapy succeeds.

Yunu’s platform standardizes the collection, labeling, and interpretation of imaging data, providing sponsors with a single source of truth. For pharmaceutical and biotech companies, that means faster insight generation, stronger regulatory evidence, and ultimately, quicker access to market for new treatments.

Turning Imaging Data into Actionable Intelligence

AI in clinical trials depends on structured, well-labeled data. Yet across most trials, imaging measurements are stored separately from source images or are missing entirely.

Yunu’s platform solves this by unifying workflows across sites and imaging CROs, capturing both the measurements and the underlying images. This integration makes imaging data usable for AI in drug development, clinical data management, and real-world evidence studies.

By connecting every stakeholder from trial sites to the sponsor, Yunu reduces inaccuracy rates by up to 30% and removes duplication across systems. The result is a more efficient, traceable, and compliant data pipeline that supports both scientific integrity and commercial growth.

As Sorenson puts it,

 “The real opportunity in AI isn’t the algorithm, it’s the data. Once you have complete, well-labeled imaging datasets, everything from model training to clinical trial design becomes faster and more reliable.”

The Role of AI in Drug Development

As AI in drug development becomes central to modern discovery, imaging data has emerged as a critical asset. Digital biomarkers and radiomics derived from imaging can reveal subtle biological patterns invisible to the human eye, accelerating the testing and validation of therapies.

Yunu’s technology enables sponsors to capture these insights at scale. By automating image measurement, annotation, and analysis, the platform transforms imaging into a real-time source of evidence that can inform study design, predict patient response, and support precision medicine strategies.

For companies investing in AI in clinical trials, partnerships with technology-driven imaging providers are no longer optional. They are essential to competitive advantage.

Reducing Clinical Trial Inefficiencies and Patient Dropout

Clinical trials remain among the most resource-intensive areas of the life sciences. Imaging-related issues account for up to a third of patient dropouts, driven by inconsistent measurements and manual workflows.

By automating imaging management, Yunu helps trial sites work more efficiently and accurately, increasing patient retention and enabling faster recruitment into new studies.

At NCI-designated cancer centers, Yunu’s adoption has already led to a measurable increase in the number of active trials and enrolled patients, without increasing staff workloads. This demonstrates how precision technology can directly improve patient access and outcomes.

Data Integrity and Regulatory Confidence

In an environment governed by strict compliance frameworks, maintaining full traceability is crucial. Yunu’s stateful data architecture provides complete provenance supporting both transparency and audit readiness. 

For sponsors navigating global regulatory requirements, this structure ensures data integrity from image acquisition to submission, helping align with MHRA, EMA, and FDA expectations. As digital biomarkers and imaging endpoints become standard in precision medicine, platforms that provide secure, transparent, and scalable imaging workflows will define the next generation of clinical data management.

Building the Future of AI in Clinical Research

Yunu represents a broader movement across life sciences: shifting from manual data capture to intelligent, automated systems that link clinical, imaging, and operational data in real time.

For leaders in pharmaceutical recruitment, biotechnology recruitment, and medical device recruitment, this evolution highlights the growing need for cross-functional teams. Teams now require AI engineers, clinical data scientists, imaging analysts, and compliance experts who can translate technology into measurable clinical impact.

As more organizations adopt AI in healthcare, new digital health jobs are emerging across data governance, clinical operations, and product development. These roles are critical to sustaining innovation and ensuring that AI adoption remains safe, ethical, and effective.

How Barrington James Supports AI and Life Sciences Growth

At Barrington James, we work with organizations driving transformation across pharmaceuticals, biotechnology, and medical devices. Our specialist recruitment teams connect employers with professionals across AI, clinical data management, and precision medicine.

We support both permanent and contract hiring across key functions:

• Clinical data management and informatics
• AI in healthcare jobs and digital health jobs
• Regulatory and quality assurance for medical devices
• Commercial and scientific leadership roles in pharma recruitment and life sciences executive search

We help our partners build the teams behind healthcare innovation, bridging clinical integrity with commercial execution. Whether you are scaling your AI capability, strengthening regulatory oversight, or hiring leaders to drive your next phase of growth, our consultative approach connects you with the right talent at the right time.

Contact our team today to discuss how we can support your next step in life sciences recruitment and innovation.