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Digital & Innovation

Call for bold investment and legislation to break healthcare barriers in AI integration

Health Industry Hub | January 22, 2024 |

Digital & Innovation: Australia’s healthcare landscape is grappling with formidable barriers hindering the widespread adoption of artificial intelligence (AI), prompting researchers in Queensland to make a compelling case for increased government funding to unlock the transformative potential of this emerging technology.

In stark contrast to the United States, where hospital-based clinical AI, encompassing sepsis or deterioration prediction, has witnessed rapid adoption over the past five years, Australia lags behind.

With the exception of digital imaging-based AI products, the majority of Australian hospitals remain clinical AI-free zones. The debate surrounding this cautious approach persists, with some lauding it as a prudent stance considering ethical, privacy, and safety concerns, while others argue that the nation might be missing out on vital interventions that could save lives and significantly improve healthcare outcomes.

Dr Anton Van Der Vegt, Advanced Queensland Industry Research Fellow with the Centre for Health Services Research at the University of Queensland Faculty of Medicine, voiced concerns over the slow pace of AI adoption.

“Across a network of clinicians in a national AI working group, only one hospital was known to have an AI trial underway. As far as we are aware, there is no clinical AI implemented across Queensland Health despite having Australia’s largest centralised EMR system, which could make large-scale AI feasible,” he and colleagues stated in a journal article.

They authors referred to a recent study which reported over 20 different implemented applications of AI in clinical settings including, among others, prediction models for stroke, hypertension, venous thromboembolism and appendicitis. Of these, 82% were implemented in the US and none in Australia.

The barriers to AI adoption in Australia’s healthcare system are multifaceted. Dr Van Der Vegt identified a lack of clinician trust and data privacy as primary obstacles to the integration of AI in clinical settings. Furthermore, concerns about potential biases in underlying data, health inequity, and the absence of robust regulation are contributing to the slow progress in implementing AI-related technology in Australian clinical settings.

Against this backdrop, Australia finds itself without specific AI legislation. The Australian government, recognising the need to address potential risks, initiated a consultation last year, releasing a discussion paper seeking input from industry and the community. While the interim response to the AI consultation was published on 17 January 2024, it does not include firm commitments and timelines to a legislative path forward for AI regulation. However, it does openly acknowledge that the current laws and regulations in Australia are not adequate to address the rapid moving nature of AI and its associated risks.

The Australian Human Rights Commission has characterised the nation’s current AI regulations as a “patchwork,” warning that inadequately developed and deployed AI can pose threats to human rights.

“AI operates in a regulatory environment that is patchwork at best. This has allowed AI to proliferate in a landscape that has not protected people from human rights harms,” the Commission emphasised, citing concerns about privacy, algorithmic discrimination, automation bias, and misinformation and disinformation.

AI algorithms face additional challenges as they are often developed and evaluated on datasets different from those at hospital sites. Dr Van Der Vegt and his colleagues underscored this issue, explaining that changes to clinical workflows, patient conditions, data quality levels, and demographic distributions can significantly impact algorithm performance.

Despite these challenges, Australian Medical Association President, Professor Steve Robson, acknowledged the transformative potential of AI in medicine, particularly in assisting with image recognition and decision support for radiologists. However, he emphasised the need for public funding outside healthcare budgets to develop standardized evaluation infrastructure.

In the midst of these challenges, the call for government funding echoes loudly. Dr Van Der Vegt and his colleagues propose a standardised prospective evaluation infrastructure coupled with a standardised AI implementation framework to empower healthcare organisations. This, they argue, would provide the necessary tools and confidence to move beyond retrospective studies and implement well-tested AI into clinical practice, revolutionising healthcare delivery in Australia.

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