AI improves mammography cancer detection rates in large cohort study

An observational, multicenter, real-world study conducted at 12 screening sites in Germany has reported a 17.6% higher cancer detection rate among women aged 50–69 who received AI-supported double-reading mammography screenings compared to those who received standard double-reading. Recall rates remained unchanged.

Mammography screening programs often rely on double reading to identify breast cancer at earlier stages. Radiologists face substantial workloads interpreting mammograms, most of which include cases with no signs of cancer. Screening centers struggle to keep up with providing efficient and accurate assessments, a problem only getting more urgent with a growing shortage of trained radiologists.

Many breast cancers elude early detection only to be diagnosed at later stages, reflecting ongoing issues with current screening methods. False positive results burden both participants and health care systems with needless worry and unnecessary follow-up (recall) appointments. Efforts to boost early detection sensitivity and lower unnecessary false positives are top priorities.

In a study titled “Nationwide real-world implementation of AI for cancer detection in population-based mammography screening,” published in Nature Medicine, researchers compared screening results of a large cohort with and without AI-assisted prediction software.

Investigators enrolled 463,094 women in the German mammography screening program (PRAIM study). Participants were divided into an AI group (260,739) and a control group (201,079). AI-based software classified certain examinations as normal and triggered a “safety net” alert for high-suspicion cases.

Results showed a higher breast cancer detection rate of 6.7 per 1,000 in the AI group compared to 5.7 per 1,000 in the control group. The recall rate was 37.4 per 1,000 with AI and 38.3 per 1,000 without AI. Recall rate measures how many participants return for further tests and includes correct initial detections as well as false positives.

Positive predictive value, the portion of suspicious findings that truly represent cancer, reached 17.9% for AI-supported reading versus 14.9% in the control group. The positive predictive value of follow-ups resulting in biopsy was 64.5% in AI-assisted screening, compared to 59.2% under standard double reading.

While the false positives were slightly lower, the researchers considered these to be comparable to existing double reading methods. That AI-supported mammograms detected more cancers without increasing recall rates in a large cohort is considered the more critical threshold for recommending further development and greater widespread use of the technology.

© 2025 Science X Network

An observational, multicenter, real-world study conducted at 12 screening sites in Germany has reported a 17.6% higher cancer detection rate among women aged 50–69 who received AI-supported double-reading mammography screenings compared to those who received standard double-reading. Recall rates remained unchanged.

Mammography screening programs often rely on double reading to identify breast cancer at earlier stages. Radiologists face substantial workloads interpreting mammograms, most of which include cases with no signs of cancer. Screening centers struggle to keep up with providing efficient and accurate assessments, a problem only getting more urgent with a growing shortage of trained radiologists.

Many breast cancers elude early detection only to be diagnosed at later stages, reflecting ongoing issues with current screening methods. False positive results burden both participants and health care systems with needless worry and unnecessary follow-up (recall) appointments. Efforts to boost early detection sensitivity and lower unnecessary false positives are top priorities.

In a study titled “Nationwide real-world implementation of AI for cancer detection in population-based mammography screening,” published in Nature Medicine, researchers compared screening results of a large cohort with and without AI-assisted prediction software.

Investigators enrolled 463,094 women in the German mammography screening program (PRAIM study). Participants were divided into an AI group (260,739) and a control group (201,079). AI-based software classified certain examinations as normal and triggered a “safety net” alert for high-suspicion cases.

Results showed a higher breast cancer detection rate of 6.7 per 1,000 in the AI group compared to 5.7 per 1,000 in the control group. The recall rate was 37.4 per 1,000 with AI and 38.3 per 1,000 without AI. Recall rate measures how many participants return for further tests and includes correct initial detections as well as false positives.

Positive predictive value, the portion of suspicious findings that truly represent cancer, reached 17.9% for AI-supported reading versus 14.9% in the control group. The positive predictive value of follow-ups resulting in biopsy was 64.5% in AI-assisted screening, compared to 59.2% under standard double reading.

While the false positives were slightly lower, the researchers considered these to be comparable to existing double reading methods. That AI-supported mammograms detected more cancers without increasing recall rates in a large cohort is considered the more critical threshold for recommending further development and greater widespread use of the technology.

© 2025 Science X Network