Unlocking the potential: National Policy Roadmap for AI in healthcare

AI in Healthcare National Policy Roadmap

At the AI.Care conference in Melbourne today, Professor Enrico Coiera, Director of the Australian Institute of Health Innovation, is presenting a ‘National Policy Roadmap for AI in Healthcare,’ urging the establishment of a National AI in Healthcare Council.

The roadmap asks for a coordinated approach to govern AI in healthcare, with a proposed unified strategy for AI safety, quality, and ethics. Key recommendations include enhancing AI literacy in healthcare, supporting the local healthcare AI industry, and bolstering research and development for evidence-based AI-driven healthcare. Professor Coiera stresses the urgency for Australia to prioritise AI governance to ensure patient safety and foster digital health sector growth.

The roadmap, developed by the Australian Alliance for AI in Healthcare (AAAiH), resulted from extensive consultations with government departments, research bodies, regulatory agencies, and industry representatives. It aims to position Australia competitively in the global healthcare AI sector, citing the substantial investments made by the UK and USA.

The proposed National AI in Healthcare Council would coordinate oversight responsibilities related to AI safety and ethics.

Professor Coiera highlighted the transformative potential of AI in improving clinical outcomes and healthcare workflows, envisioning a more agile, adaptive, and personalised healthcare system.

”Artificial Intelligence has benefits too big to ignore. Taking advantage of these benefits will however require a mature and co-ordinated national approach.”

Professor Enrico Coiera, Director of the Australian Institute of Health Innovation

According to the analysis of the AI opportunity provided in the National Policy Roadmap, AI-enabled gains in healthcare are propelled by global investments in electronic health records, abundant clinical datasets, affordable computational power, and advancements in deep learning technologies.

Machine learning analyses diverse health data, aiding in patient-specific information analysis, disease diagnosis, and treatment suggestions. Large language models streamline documentation creation, reducing administrative burdens on clinicians. AI facilitates planning by optimising treatment regimes and dynamically updating plans. In communication, AI translates complex information, and chatbots engage in structured conversations for tasks like information retrieval or cognitive behavioural therapy.

Additionally, AI-driven discovery explores new knowledge, uncovering scientific laws, genetic associations, or drug repurposing.

The roadmap will be discussed in a panel at the Australasian Institute of Digital Health conference, and a public webinar is scheduled for November 29, 2023…Register for the webinar here.


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