UK Research and Innovation will invest 10 million pounds ($13 million U.S.) to develop artificial intelligence (AI) and scalable MRI services to diagnose liver disease.
The investment is being made as part of the government's Industrial Strategy Challenge Fund to develop a national digital radiology and pathology infrastructure, with supplementary investment from GE Healthcare and image analysis software developer Perspectum Diagnostics.
Liver disease affects up to 25% of the adult population in the U.S. and U.K. Due to the invasive nature of the current gold standard, biopsy, many patients are not diagnosed or are diagnosed late. New MRI technologies, such as Perspectum's LiverMultiScan, can help identify and monitor disease noninvasively, according to the company.
Perspectum and GE Healthcare will now form part of the National Consortium for Intelligent Medical Imaging (NCIMI), with a focused project on liver and metabolic health. The development of AI-enabled tools that automate the assessment of liver iron load will aid in the early diagnosis of hemochromatosis before patients develop end-organ complications, such as cancer and diabetes.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
