معرفی
Dr Anton Van Der Vegt is an Advanced QLD Industry Research Fellow with the Centre for Health Services Research at the University of Queensland Faculty of Medicine. He holds affiliate positions with both the Centre for Health Services Research and the Queensland Digital Health Centre within the Faculty of Health, Medicine and Behavioural Sciences.
His educational background includes a Bachelor (Honours) of Engineering in Mechanical from the University of Sydney and a PhD from The University of Queensland focused on the application of AI with information retrieval to support clinical decision making.
Dr Van Der Vegt's research focuses on the intersection of artificial intelligence and healthcare delivery. His work spans developing AI algorithms for patient deterioration prediction, implementing clinical decision support systems, and addressing challenges in electronic medical record data extraction and validation. He has particular expertise in translating AI research into practical healthcare applications, with a focus on sepsis prediction, patient monitoring systems, and clinician-AI interaction design. His recent publications reveal a strong emphasis on practical implementation challenges of AI in healthcare settings, including systematic reviews of implementation barriers, frameworks for clinical AI deployment, and studies on clinician adoption of AI-enabled decision support.
- Advance Queensland Industry Research Fellowship
- Co-author of two US patents
Dr Van Der Vegt is actively supervising multiple PhD students working on projects related to clinical AI implementation, including sample sub-group algorithm bias analysis, methods for presenting AI predictions to clinicians, EMR data extraction, and evidence-based large language models as health information mediators. He has secured significant funding including the Advance Queensland Industry Research Fellowship (2022-2025), NASCENT: National infrastructure for real-time clinical AI trials (2024-2029), and Rise--For Recovery (2025-2027).
He leads collaboratory projects with Queensland Health to develop and implement AI algorithms for identifying patients at risk of deterioration in hospital general wards and serves as a co-investigator on a Queensland Health sepsis prediction project.
