
An international group of scientists are questioning a study that reported that an artificial intelligence (AI) algorithm developed in part at Google diagnosed breast cancer on mammography images more accurately than radiologists, according to an opinion piece published October 14 in Nature.
The original study was published January 1 in Nature, and it involved the analysis of an AI algorithm in interpreting mammograms from 26,000 U.K. National Health Services (NHS) hospitals. The study was conducted by a research group that included scientists from Google Health, DeepMind, Imperial College London, the NHS, and Northwestern University
The study found that AI correctly identified cancers from the images with a similar degree of accuracy to expert radiologists, and AI also reduced the proportion of screening errors.
But in the new opinion piece, scientists took the study to task. They believe that restrictive data access procedures, a lack of published computer code, and unreported model parameters make it impractically difficult for any other researchers to confirm or extend this work, according to the authors.
On a broader basis, the editorialists said that a lack of transparency in publishing AI algorithms for health applications is concerning. Furthermore, taking appropriate measures to protect patient privacy while allowing other researchers to contribute and correct potential errors that could result in poor outcomes for patients causes tension among scientists, they wrote.
![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&fit=crop&h=100&q=70&w=100)







![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&fit=crop&h=112&q=70&w=112)









