AI improves breast cancer detection accuracy for radiologists when reading screening mammograms, helping them devote more of their attention to suspicious areas, according to a study published in Radiology. Previous research has shown that AI for decision support improves radiologist performance by increasing sensitivity for cancer detection without extending reading time. However, the impact of AI on radiologists’ visual search patterns remains underexplored.

To learn more, researchers used an eye tracking system to compare radiologist performance and visual search patterns when reading screening mammograms without and with an AI decision support system. The system included a small camera-based device positioned in front of the screen with two infrared lights and a central camera. The infrared lights illuminate the radiologist’s eyes, and the reflections are captured by the camera, allowing for computation of the exact coordinates of the radiologist’s eyes on the screen. 

By analyzing this data, we can determine which parts of the mammograms the radiologist focus on, and for how long, providing valuable insights into their reading patterns, said the study’s joint first author Jessie J. J. Gommers, MSc, from the Department of Medical Imaging, Radboud University Medical Center in Nijmegen in the Netherlands.

Radiologists seemed to adjust their reading behavior based on the AI’s level of suspicion: when the AI gave a low score, it likely reassured radiologists, helping them move more quickly through clearly normal cases, Gommers said. Conversely, high AI scores prompted radiologists to take a second, more careful look, particularly in more challenging or subtle cases.

Source: https://www.rsna.org/news/2025/july/ai-spots-more-lesions-on-mammograms