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admin Mar 9, 2026
Eye tracking date reveals how AI alters radiologists’ visual search patterns
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(A) Fixations (pink circles) of a radiologist while reading without and with AI support on a screening mammogram in a 72-year-old woman without breast cancer. The radiologists did not recall the woman in either reading condition. The AI tool classified this examination as low risk, with a maximum region score under 40. (B) Fixations of a radiologist while reading without and with AI support on a screening mammogram in a 50-year-old woman with invasive carcinoma. The radiologist recalled this woman in both reading conditions. The AI tool classified this examination as very high risk, with a maximum region score of 80 or higher (red numbers; yellow number represents intermediate risk). Diamonds indicate calcifications, and circles denote soft tissue lesions.
https://doi.org/10.1148/radiol.243688 ©RSNA 2025
The AI’s region markings functioned like visual cues, Gommers said, guiding radiologists’ attention to potentially suspicious areas. In essence, she said, the AI acted as an additional set of eyes, providing the radiologists with additional information that enhanced both the accuracy and efficiency of interpretation.
“Overall, AI not only helped radiologists focus on the right cases but also directed their attention to the most relevant regions within those cases, suggesting a meaningful role for AI in improving both performance and efficiency in breast cancer screening,” Gommers said.
Gommers noted that overreliance on erroneous AI suggestions could lead to missed cancers or unnecessary recalls for additional imaging. However, multiple studies have found that AI can perform as well as radiologists in mammography interpretation, suggesting that the risk of erroneous AI information is relatively low.
To mitigate the risks of errors, Gommers said, it is important that the AI is highly accurate and that the radiologists using it feel accountable for their own decisions.
“Educating radiologists on how to critically interpret the AI information is key,” she said.
The researchers are currently conducting additional reader studies to explore when AI information should be made available, such as immediately upon opening a case, versus on request. Additionally, the researchers are developing methods to predict if AI is uncertain about its decisions.
“This would enable more selective use of AI support, applying it only when it is likely to provide meaningful benefit,” Gommers said.
For More Information
Access the Radiology article, “Influence of AI Decision Support on Radiologists’ Performance and Visual Search in Screening Mammography,” and the related editorial, “Behavioral Research Will Help Us Understand When AI Will Help Radiologists.”
Read previous RSNA News stories about breast imaging:
