AI-based BRAIx risk score for the intermediate-term prediction of breast cancer: a population cohort study.

Helen ML Frazer; John L Hopper; Tuong L Nguyen; Michael S Elliott; Katrina M Kunicki; Osamah Al-Qershi; Daniel F Schmidt; Enes Makalic; Shuai Li; Samantha K Fox; James G Dowty; Carlos A Peña-Solorzano; Chun Fung Kwok; Yuanhong Chen; Chong Wang; Jocelyn Lippey; Peter Brotchie; Gustavo Carneiro; Fredrik Strand; Davis J McCarthy

Journal article

AI-based BRAIx risk score for the intermediate-term prediction of breast cancer: a population cohort study.

Helen ML Frazer, John L Hopper, Tuong L Nguyen, Michael S Elliott, Katrina M Kunicki, Osamah Al-Qershi, Daniel F Schmidt, Enes Makalic, Shuai Li, Samantha K Fox, James G Dowty, Carlos A Peña-Solorzano, Chun Fung Kwok, Yuanhong Chen, Chong Wang, Jocelyn Lippey, Peter Brotchie, Gustavo Carneiro, Fredrik Strand, Davis J McCarthy

Lancet Digit Health | Elsevier BV | Published : 2026

DOI: 10.1016/j.landig.2026.100987

Abstract

BACKGROUND: Artificial intelligence (AI)-based algorithms are being implemented in breast screening to detect breast cancers on mammographic images. We aimed to apply an epidemiological approach to demonstrate how a cancer detection algorithm can be leveraged as an intermediate-term predictor of breast cancer (current and 4-year risk) to deliver greater risk-based personalisation in screening mammography. METHODS: In this population cohort study, we used detection scores from an AI cancer detection algorithm (BRAIx AI Reader), which was calibrated using a training dataset of 397 648 women aged 40 years to 97 years from women who screened at BreastScreen Victoria, Australia between Jan 1, 201..

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