Opportunity: Implementation of Artificial Intelligence (AI) technologies in health care services is challenging with an absence of best-practice guidance, presenting an urgent need to bring together stakeholders who have a vested interest in the opportunities to establish a coherent, collaborative, and sustainable national approach supporting the implementation of AI in the Australian health system.
Opportunity: This was a feasibility project that explored leveraging large language models (LLMs) to create a dashboard prototype for identifying and analysing emerging medication shortage-related trends in social media.
Opportunity: Learning from the PreHaRM experience, identifying points of convergence and disconnect, between policy and practice is critical for reducing implementation delays, and ensuring that AI technologies are introduced in ways that genuinely support clinical staff and improve patient outcomes.
Opportunity: This project explored how health consumers (e.g., patients, carers, public) perceive the use of Artificial Intelligence (AI) in digital health, with a focus on AI-generated summarisation of health information. As AI tools become increasingly integrated into healthcare systems, understanding how they are received by patients, carers, and the public is critical to ensure safe, ethical, and effective adoption in Australia.
Opportunity: Existing AI ethics frameworks have been largely theoretical and difficult to operationalise within complex healthcare environments. This project addressed this by developing and testing a practical governance framework and AI maturity model within a leading Australian health service.
Opportunity: address barriers to optimal care for individuals living with genetic disorders through the development and integration of a scalable digital solution enabling clinical genetics services (CGS).
Opportunity: Establish and deploy an integrated, interoperable and secure scalable platform which could harmonise multimodal healthcare data for data-driven research and development. The project also looked to create a use case to validate platform efficacy.
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