Rate My Professor Sebastiano Barbieri

Barbieri's research interests center on machine learning in healthcare, encompassing risk prediction using electronic medical records, medical image processing, and the safe incorporation of AI into clinical decision-making. He is a proponent of responsible AI methodologies, such as synthetic data generation and federated learning, to improve data privacy and accessibility in digital health. His publication record includes "Predicting cardiovascular events from routine mammograms using machine learning" (Heart, 2026), "Initiators of Semaglutide in General Practice in New South Wales, 2020–2023: A Retrospective Cohort Study" (Heart Lung and Circulation, 2026), "Acquisition-independent deep learning for quantitative MRI parameter estimation using neural controlled differential equations" (Medical Image Analysis, 2026), "Travel times and distances to health services in Australia" (Scientific Data, 2025), "Aortic valve leaflet motion for diagnosis and classification of aortic stenosis using single view echocardiography" (Journal of Cardiovascular Imaging, 2025), "Estimating 5-year absolute risk of cardiovascular disease using routinely collected electronic medical records from Australian general practices" (Heart, 2025), "A scoping review of the governance of federated learning in healthcare" (npj Digital Medicine, 2025), "Generative AI mitigates representation bias and improves model fairness through synthetic health data" (PLoS Computational Biology, 2025), and "Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV" (Journal of Biomedical Informatics, 2023). Barbieri supervises PhD and MPhil students on digital health infrastructure and AI impacts on clinical workflows, and coordinates the UQ course Digital Health in Action (CIDH7302).