AI Systems Safety
Digital health and AI are integral to the modern-day transformations of health systems to improve quality and safety. Digital technologies are a key enabler for encouraging patients to actively participate in care processes for diagnosis, treatment and prevention.
At the same time, digital technologies can introduce new, often unforeseen, modes of failure that affect the safety and quality of care and lead to patient harm and death. Unlike other risks to patient safety, AI can, because of its scale and scope, increase the risk of harm to many patients during the delivery of health care. With large and complex AI systems being rapidly deployed, the opportunity for patient harm can be significantly increased if safety of the AI systems themselves is not improved.
Our AI Systems Safety research program takes a cross-disciplinary approach to improve the safety of digital health and AI by making theoretical, methodological and policy contributions which translate into changes in healthcare delivery and improved patient outcomes.
We are investigating the safety risks of digital health and AI technologies. Our goal is to design sociotechnical solutions to mitigate these risks and to develop new methods for the timely detection of, and response to, emerging threats.
We welcome clinicians, information technologists, engineers and students who wish to pursue excellence in health and biomedical informatics and share our passion for improving the safety of digital health and AI through their effective integration in real-world healthcare settings.
For more information or to join our team
Contact Professor Farah Magrabi: farah.magrabi@mq.edu.au
Team members
| Professor Farah Magrabi | Stream Lead |
| Dr Ying Wang | Research Fellow |
| Dr Brette Blakely | Research Fellow |
| Dr David Lyell | Research Fellow |
| Dr Sam Freeman | Research Fellow |
| Ms Saba Akbar | PhD Candidate |
| Dr Nino Susanto | PhD Candidate |
| Ms Cynthia Wong | Masters Candidate |
| Ms Tam Marwood | Masters Candidate |
Selected stream projects
- Evaluating AI in real-world clinical settings
- How machine learning is embedded to support clinical decision making
- Explainable AI in healthcare
- Automation in nursing decision support systems
- AI-enabled clinical decision support in resource constrained settings
- Classifying patient safety incidents involving digital health
- Automated identification of reports about patient safety incidents
- Automated detection of health IT failures
Research Centre
Content owner: Australian Institute of Health Innovation Last updated: 05 Jul 2024 9:46am