Redefining AI to revolutionise healthcare
Under the theme of digital health imaging, we are developing next-generation AI technologies to revolutionise healthcare with the capacity to improve diagnostic efficiency and accuracy. With this, we aim to:
- bridge gaps between the increasing needs for medical practitioners and limited medical resources
- monitor disease progression at a subclinical level to inform personalised treatment planning and patient management
- further our understanding of disease and continue to pioneer innovations in healthcare and biotechnologies
- investigate more advanced AI solutions that support the shift from hospital-based to virtual-based care
We are a group of researchers across the University of Sydney, covering a broad range of multi-disciplinary research areas related to AI and healthcare. Under the umbrella of Digital Health Imaging, we are united by our close and productive multidisciplinary collaboration, a highly collaborative clinical research environment, and a strong link to industry partners.
Collaborate with us
Underpinning our leading AI research, we will expand on the AIS platform, designing and implementing a suite of digital tools to accelerate AI imaging research, better utilise the university’s hardware investments, securely collaborate with health and commercial partners, and provide a provenance trail for establishing trust.
In this project, we will study the research area “explainable AI” that enables the outputs from the AI systems to exhibit a certain degree of explainable capability in order for clinical experts to better understand how AI systems diagnose diseases. The feedback from the clinical experts could in turn help us to develop better deep …
This project aims to exploit advanced AI technologies to enhance the quality of MRI data to make it comparable to that obtained from time consuming high-quality research protocols. The increased image data quality opens up the possibility of reliably applying advanced analysis methods to standard clinical data. The methods will be demonstrated in a number …
This MRFF project seeks to build a novel, hybrid AI learning ecosystem to generate clinically-relevant biomarkers of disease progression for the common, disabling neurological condition, multiple sclerosis (MS). The MSBASE-XNAT imaging repository and I-MED clinical radiology site data will respectively form the key components of a unique central-federated AI learning environment, yielding algorithms that, validated …
In this project, we will investigate how to introduce human intelligence in the loop when developing advanced AI methods, to progressively improve disease diagnosis results by taking advantage of both AI and human intelligence.
In this project, we will focus on cohesively developing advanced AI technologies to revolutionise imaging-based diagnosis of disease and extend our work towards commercial applications and clinical deployment. Specifically, we will first target three disease area cohorts where AI can greatly assist medical professionals: cancer (breast, brain, melanoma), lung diseases (e.g., COVID-19), and neurological conditions.