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Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Despite the perceived advantages, numerous challenges remain in the interpretation of screening mammograms. First, the high volume of screenings, combined with the requirement for independent, blinded double-reading by radiologists, places significant pressure on the existing radiology workforce [3]. Second, high false-positive recall rates on initial screening often lead to additional procedures and cause undue anxiety for the patient [4].

Serene Goh, Rachel Sze Jen Goh, Bryan Chong, Qin Xiang Ng, Gerald Choon Huat Koh, Kee Yuan Ngiam, Mikael Hartman

J Med Internet Res 2025;27:e62941

Feasibility of a Reinforcement Learning–Enabled Digital Health Intervention to Promote Mammograms: Retrospective, Single-Arm, Observational Study

Feasibility of a Reinforcement Learning–Enabled Digital Health Intervention to Promote Mammograms: Retrospective, Single-Arm, Observational Study

While rates have since rebounded to close to prepandemic levels, it is estimated that it could take as long as 22 weeks to clear the backlog of delayed mammograms [9]. While there is some debate over appropriate mammogram usage given drawbacks associated with overdiagnosis [10], the gap between recommended and actual screening behavior is likely to persist regardless of adjustments to the recommendations, suggesting the need for behavioral interventions targeting those who remain overdue for mammograms.

Amy Bucher, E Susanne Blazek, Ashley B West

JMIR Form Res 2022;6(11):e42343