Improving breast cancer risk prediction with AI

Published Catarina de Freitas on

Monday Lunch Live

30 March 2026 (Video recording below) 

Improving breast cancer risk prediction with AI

Associate Professor Davis McCarthy showcases the BRAIx project—an innovative academic-clinical partnership leveraging deep learning to enhance early detection and personalise screening pathways.

Using data from more than 95,000 women and advanced ensemble neural networks, BRAIx generates calibrated risk scores from mammogram images, offering a powerful tool for identifying high-risk individuals and tailoring screening intervals. This webinar highlights the potential of BRAIx to transform population-level screening into a more precise, risk-based model, with real-world validation across Australian and Swedish cohorts.

Explore the future of AI-driven healthcare and its life-saving implications for breast cancer prevention. Gain insights into the limitations of current screening programs and how AI can address challenges such as false positives, missed cancers, and flat participation rates. 
 

Chair 

Dr Allison Drosdowsky
Postdoctoral Research Fellow
Department of General Practice and Primary Care, University of Melbourne

Allison's research aims to use large linked datasets including general practice medical record data to improve the timely diagnosis of cancer. Her background is in biostatistics, and her research interests include novel statistical methodologies, the translation and implementation of research, and meta-research. In addition, she has been a Health Services Researcher at the Peter MacCallum Cancer Centre since 2012, where she is involved in the development and execution of statistical analysis plans, and advising on study design and methodology.

Speaker

A/Prof Davis McCarthy
Head, Bioinformatics and Cellular Genomics
St Vincent's Institute of Medical Research

A/Prof McCarthy has contributed to popular methods for the analysis of differential expression in RNA-sequencing data, and recently developed methods for the analysis of single-cell RNA-seq data.

A/Prof McCarthy primarily implements methods in the R and Python languages and publishes open-source software packages through the Bioconductor project.He is interested in studying the effects of DNA variation on gene expression measured in individual cells. He explores single-cell genetics in two ways: by studying effects of common DNA variation on single-cell gene expression (single-cell quantitative trait locus mapping) and by studying the effects of somatic DNA mutations on single-cell gene expression (clonal cell populations). The former provides information about genetic regulation of natural gene expression variation, while the latter informs us about the effects of DNA accumulated mutations in tissues that are relevant both to healthy ageing and to cancer.

A/Prof McCarthy enjoys collaborating with biologists and other researchers to contribute computational and data analysis expertise to biologically focused studies.

Resource details

breast scan close up
Course type
Webinars
Duration
60 mins
Price
$0.00
Curriculum Area
Prevention, screening and diagnostics
Research (incl. Clinical Trials)
Clinical Care
Speciality
Clinician
Consumer / patient / carer
Early to mid career researcher
Education & Training
Nurse
Senior researcher / scientist
Breast
Clinical care
Monday Lunch Live
Research

This course is brought to you by

Alliance members