Melissa Barron

Keywords Breast cancer Screening Mammography Reader training Data science AI Get full text access Log in, subscribe or purchase for full access. References 1. Nelson, H.D. ∙ Fu, R. ∙ Cantor, A. ... Effectiveness of breast cancer screening: systematic review and meta-analysis to update the 2009 U.S. Preventive Services Task Force Recommendation Ann Intern Med. 2016; 164(4):244-255 2. Rawashdeh, M.A. ∙ Lee, W.B. ∙ Bourne, R.M. ...

1 Introduction Breast cancer remains the most prevalent cancer in women worldwide, and the incidence of the disease continues to increase annually [1]. Globally, more than three million new cases of breast cancer are predicted to be diagnosed annually by 2040 [2]. Current estimates show that one in eight women will be diagnosed with breast cancer in their lifetime, and one in 40 affected women will die from the disease [3].

Integrating learning spacing in medicine has shown promise in enhancing knowledge retention and diagnostic proficiency. While studies demonstrate the effectiveness of spaced learning in various fields, limited research exists on its application in radiological training. This study aims to investigate the impact of intervals in spaced training on radiologists' and trainees' diagnostic performance via mammogram test sets.