Ibex Scoops Second Win of The UK AI Award with Leading NHS Trusts

NHS Invests £123 million in AI, Accelerating Nationwide Adoption Including Deployment of Ibex’s Galen™ Breast Solution Across Five NHS Trusts

Ibex Medical Analytics (Ibex), the leader in AI-powered cancer diagnostics, University of Nottingham and a consortium of NHS Trusts, announced today they are winners of the UK Artificial Intelligence in Health and Care Award, enabling the deployment of Ibex’s Galen™ Breast solution across five National Health Services (NHS) trusts and delivering on their aligned missions of improving breast cancer diagnosis and lab efficiency.

Galen Breast supports pathologists by providing AI-based tools and insights that help detect and grade different types of breast cancer. The solution will be deployed at Nottingham University Hospitals, Cambridge University Hospitals, North West Anglia NHS Foundation Trust, Betsi Cadwaladr University Health Board and University Hospitals Birmingham. Pathologists from each trust will use Ibex’s Galen Breast to analyze a total of 10,000 biopsies as part of routine practice and evaluate how the Ibex technology helps improve the quality of diagnosis, reduce case review time, and impacts overall cost-effectiveness of breast cancer diagnosis and turnaround time for patients. With increasing attention on the potential to improve people’s health through digital technologies, both patient and public involvement will be core to the project, led by the Oxford Academic Health Science Network.

“AI has the potential to speed up diagnoses and treatments and free up time for our doctors and nurses so they can focus on caring for patients,” said Steve Barclay, the UK Health and Social Care Secretary.
The Phase 4 awards enable AI technologies that have market authorization to generate important evidence targeting large-scale commissioning or deployment. As the most mature category, with only two Phase 4 projects granted in 2023, the award will enable Ibex and its partners to demonstrate clinical and economic utility of Galen Breast in real-world implementations in the NHS.

Professor Emad Rakha, Honorary Consultant Pathologist at the University of Nottingham and Nottingham University Hospitals NHS trust, and the study’s principal investigator added: “Over the last several years in the UK, cancer cases increased while the number of pathologists decreased, resulting in record-high workloads for pathology departments. Timely and accurate diagnosis can significantly impact breast cancer survival rates, making Ibex’s solution a vital and welcome addition into NHS trusts.”

Galen Breast was developed by a team of pathologists, data scientists and software engineers who applied advanced Deep Learning technologies, training an AI algorithm on millions of image samples. The solution helps pathologists identify more than 50 breast-specific features and demonstrated excellent outcomes across multiple clinical studies, including a recently published study in Nature’s peer-reviewed npj Breast Cancer journal(1),(2).

“We are proud to receive this award from the NIHR and NHS, said Chaim Linhart, PhD, Co-Founder and Chief Technology Officer of Ibex Medical Analytics. “This award and other programs supported by the UK government signify its commitment to making the NHS the global leader in implementing AI technologies in healthcare. Our trusted and robust AI platform is already helping UK pathologists improve the quality of prostate cancer diagnosis, and we are eager to work with our NHS partners on expanding our collaboration to support breast cancer diagnosis and treatment.”

Ibex was also a winner of the AI Award in 2020, enabling a broad roll out of Galen Prostate in six NHS hospitals, including Imperial College London, University College London and University Hospitals Coventry & Warwickshire. Ibex’s solutions are deployed in multiple pathology departments in the UK, including Betsi Cadwaladr University Health Board in Wales and SourceLDPath, supporting pathologists in the diagnosis of cancer with improved quality and turnaround times.

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