Qure.ai and Project Data Sphere, a nonprofit initiative of the CEO Roundtable on Cancer, are partnering to improve tumor assessments using AI-enabled tools for clinical trials and cancer care.
The collaboration aims to increase efficiency and consistency in evaluating the effectiveness of cancer treatments through the use of autoRECIST, a product of Project Data Sphere’s Images and Algorithms program. The program, which began in consultation with the U.S. Food and Drug Administration (FDA), includes stakeholders and experts from across the pharmaceutical industry and academia.
Through the partnership, autoRECIST will address the need for automating and standardizing tumor response assessments in medical imaging to improve cancer treatment and research. The resulting AI tool will assist radiologists in the detection, selection, measuring, and tracking of lesions using defined criteria, according to Qure.ai.
![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.auntminnie.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)
