Industry
Healthcare
Solution
Artificial intelligence (AI)
Australia’s health and wellbeing leaders increasingly recognise that artificial intelligence (AI) can solve some of the sector’s deepest systemic challenges. Now, a clear path forward is needed to build trust in the technology and bring leaders and workers into the sector’s AI-driven reinvention.
Health and wellbeing leaders plotted a suggested way forward—covering governance, vision, technology readiness, and workforce preparation—at five national roundtables hosted by PwC Australia and the University of Technology Sydney (UTS).
The events brought together 133 leaders from the health and wellbeing sector to explore practical ways to bridge the divide between Australia’s AI ambition and reality. The main takeaway? AI presents risks that must be carefully managed in a high-stakes environment such as healthcare, but most leaders now see AI as a way to improve health outcomes and operational efficiency—if the human-related hurdles of trust and workforce readiness can be overcome.
“The future of healthcare depends on those who collaborate and adapt. Leveraging emerging technologies combined with deep industry expertise will be critical to both transforming our organisations and closing the AI adoption gap.”
A sector in need of reinvention Technology meets opportunity
The sector’s adoption of AI has been slower than most but is beginning to grow at just the right moment for Australia’s strained health and wellbeing sector.
Despite decades of reform and significant investment, the sector continues to grapple with converging challenges: an ageing population placing increasing pressure on services, a surge in chronic disease, persistent workforce shortages, financial sustainability concerns, clinical burnout, and stark geographic disparities in access and outcomes.
These issues are only compounded by five powerful global megatrends reshaping the world around us. The rise of emerging infectious diseases is testing the resilience of public health systems. Global supply chain vulnerabilities are disrupting access to critical medical supplies and pharmaceuticals. Rapid technological innovation is transforming care delivery and inviting new, non-traditional players into the sector. Meanwhile, digital transformation is outpacing the sector’s ability to upskill its workforce, exposing a growing gap in digital fluency.
Together, these forces are creating a complex, high-stakes environment that demands bold thinking, cross-sector collaboration, and a reimagining of how we deliver care.
AI is increasingly recognised as a strategic lever for sector reinvention—and adoption is increasing despite lingering trust issues among some CEOs, according to PwC’s Annual Global CEO Survey.
In 2024:
Fast forward a year, and in 2025:
Growing evidence of AI’s potential The rise of AI in healthcare
AI is already bringing bold new solutions into the Australian health and wellbeing sector, building on the sector’s steadily advancing use of all types of AI-driven technologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP).
From improving diagnostic accuracy to streamlining administrative tasks, the last decade has given rise to more examples of AI delivering measurable outcomes in operational efficiency, equity and access, and clinical breakthroughs.
“An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.”³
Operational efficiency
AI can alleviate the growing administrative burden on healthcare systems in a number of practical ways. Implementing low risk, high impact applications provide immediate progress and can help secure executive support for wider AI adoption. These applications can be pursued in parallel with complex clinical use cases, which may require more careful risk management and consumer engagement.
- Transcription: Ramsay Health Care is piloting an AI-powered clinical documentation and scribing tool designed to reduce the time clinicians spend on manual notetaking
- Clinical documentation and coding: Machine learning on AWS enables 3M Health Information Systems to streamline clinical documentation and billing workflows
- Billing: One attendee at our roundtable events spoke of using a Large Language Model (LLM) to automate billing, achieving 97% accuracy and identifying over AU$1 million in potential savings from small improvements.
- Rostering and scheduling: AI is being applied to optimise rostering and scheduling, reducing staff time and more efficiently matching employee parameters with service requirements.
Equity and access
Improving accessibility for underserved populations and helping healthcare facilities predict and plan for spikes in demand are just two ways AI is helping to bring healthcare to those who need it most, when they need it.
Rural and remote equity: Microsoft and DrumBeat.ai are piloting an initiative using AI and the Azure cloud to enhance the recognition of ear disease in rural and remote areas of Australia, with the aim to improve triage, treatment, and specialist referral of Aboriginal and Torres Strait Island children at risk of hearing loss.
Translation: At the UTS Australian Stuttering Research Centre, AI-driven tools will enable free, personalised online treatment platforms that can be translated into any language and be delivered from anywhere in the world.
Triage: Google Health is pioneering AI models to predict hospital conditions, including patient readmissions and treatment outcomes. One case study demonstrated that an AI model anticipated doctors’ prescribing decisions 75% of the time.
Clinical breakthroughs
AI is supporting faster, earlier, and potentially more accurate diagnosis, with promising results in cancer detection and hard-to-diagnose conditions.
Detection: Innovative clinical trials, like the one at St Vincent's Hospital Melbourne are paving the way for AI to enhance breast cancer detection, showcasing the transformative potential of the technology for improving diagnostic accuracy and providing more person-centred care.
Diagnostics: More advanced diagnostic applications are emerging, such as using AI to identify pulmonary fibrosis—an often-misdiagnosed lung condition—via the Open-Source Imaging Consortium’s groundbreaking database of lung scans.
Communication: Real-time AI translation tools are breaking down language barriers in multilingual healthcare settings, while groundbreaking innovations like brainwave-to-speech interpretation are opening up new possibilities for non-verbal patients.
Treatment: Machine learning and data analytics technologies are improving children’s cancer treatment by predicting the likelihood of relapse and assisting clinicians in making more informed decisions with unprecedented breadth and accuracy.
“What we are seeing happening already is where we can apply an AI tool to help us to improve communication so that every patient who comes in front of a clinician, regardless of where they come from, regardless of what language they speak, and regardless of what conditions they might have that make communication difficult, there is an enabler of that communication. This can ensure communication is two way and they are being understood by the healthcare worker, and that, similarly, the healthcare worker’s communication with the patient can be well understood by the person at the other end.”
Major hurdles are not technical, but human The barriers to AI adoption
Despite these advances, health and wellbeing has the lowest AI adoption of any sector in Australia1.
To understand why, we asked leaders at our national roundtables to rank, by difficulty, the barriers to AI adoption4 (Figure 1).
Trust, privacy and ethics concerns are major inhibitors to AI adoption in Australia’s health and wellbeing sector.
Figure 1
Source: PwC Health rountable data, 2024 n=82
It turns out, the two biggest barriers are not technical, but human: trust, privacy and ethics concerns, and workforce education and resistance to change:
Trust, privacy and ethics concerns
Health and wellbeing leaders disclosed a number of trust-related issues related to AI. These included concerns that AI might “dumb down” patient assessments or reduce care to generic consultation templates. Some are worried about misinformation as the public becomes increasingly reliant on AI to check symptoms or seek health advice, while others raised doubts about how AI integrates into clinical decision-making.
The safe and ethical storage and use of healthcare data is another concern, emphasising the need for robust cybersecurity measures.
Workforce education and resistance to change
Leaders acknowledged that the health and wellbeing sector tends to resist change and the adoption of new technologies, not just AI. The reasons for such hesitation are varied—ranging from the need for better education and workforce upskilling and concerns about job replacement to a disconnect between decision-makers and patient care.
That said, leaders also recognised that this barrier is the easiest to overcome through effective change management, training and support. Research from UTS Human Technology Institute confirms this. A study that looked at AI’s introduction into workplaces found that many workers initially had low awareness of AI’s role in their jobs, despite growing familiarity with it in their personal lives. As the study progressed, workers realised AI was embedded in their routines—often unnoticed—helping them develop a more informed understanding of the role of AI in their industries.
Clearer communication about AI’s presence in specific contexts can help move workers along a spectrum from invisible bystanders who are not involved in AI decision-making to more meaningful understanding that supports smoother AI adoption.
“We need to understand not just the potential benefits, but the potential risks, and how we mitigate those.”
“Our modelling shows the region's economy could be up to 12% bigger by 2035 if it catches an AI-fuelled productivity wave and seizes decarbonisation opportunities."
Of course, technology barriers exist too. Data was a consistent theme in the roundtable discussions, specifically, the challenges of working with outdated infrastructure, particularly in hospitals; poor data quality; and the lack of interoperability between systems, especially across primary and secondary care. Some leaders admitted that continued reliance on antiquated tools like fax machines for referrals and largely paper-based electronic medical records make the integration of AI feel like a distant goal.
These technology infrastructure issues also drive-up implementation costs. Limited funding and capital were called out as major obstacles to upgrading systems or piloting new technologies, especially if the returns on investment are unproven. This is only exacerbated the sustained underinvestment in healthcare infrastructure called out by many leaders.
Four factors for successful AI implementation
The challenges raised during these discussions are real, but not insurmountable—if health and wellbeing leaders focus on the following four critical success factors for AI adoption.
1. Establish robust AI policy and governance frameworks
Trust is foundational, particularly in the health and wellbeing sector. Strong AI policy and governance frameworks can build trust and establish clear guidelines for the use of AI.
What to do: Leverage already-existing data and security policies and controls to ensure the integrity and responsibility of AI systems. Explainable AI, such as that used by The Yield in the agriculture sector, can build confidence by demystifying how decisions are made.
Case study: Explainable AI fosters trust
Trust in AI is not only an issue in the health and wellbeing sector. Australian tech start-up, The Yield, is harnessing “explainable AI” to enhance transparency in its predictive models for agriculture
The Yield gives fruit and vegetable growers clear information on the accuracy and risks of its AI predictions, helping them make more informed decisions. The approach, developed in partnership with the UTS Data Science Institute, shows how explainable AI can significantly deepen trust in AI by clearly communicating AI’s capabilities and its limitations.
2. Define your strategic AI ambition and vision
An AI ambition that clearly defines the vision for AI and how it aligns with an organisation’s strategic goals can ensure initiatives drive meaningful outcomes.
What to do: Identify the problems AI can solve, from administrative inefficiencies to clinical applications, and begin with low-risk use cases, scaling as confidence grows to secure buy-in and longer-term investments.
“When you define your AI ambition, avoid starting with a list of use cases. The health organisations that are leading on AI adoption start with their biggest strategic priorities and reimagine how AI can transform the way that those outcomes are achieved.”
3. Evaluate your technology and data readiness
Trust is foundational, particularly in the health and wellbeing sector. Strong AI policy and governance frameworks can build trust and establish clear guidelines for the use of AI.
To accelerate AI adoption, organisations need to assess their readiness, invest in scalable enterprise platforms, and establish strong data foundations. But it’s important to note that even imperfect data can be useful.
What to do: Start by piloting AI with off-the-shelf models to show value, collaborating with vendors to lower costs and speed up implementation.
“We are at the precipice of healthcare reinvention—where self-driven AI adoption is accelerating much faster than organisations can realistically support. Without a deliberate and measured approach, we may find ourselves in the midst of an AI clean-up within 18 months, grappling with the fallout of fragmented and unsupervised AI capabilities.”
4. Build and empower an AI-enabled workforce
AI is not here to replace clinicians—it’s here to support them. Upskilling and education are essential—from micro-credentials to AI literacy programmes, education fosters trust and enables safe, effective adoption.
What to do: Build an AI-enabled workforce by investing in AI literacy and empowering employees across the organisation to confidently apply AI for meaningful impact. Leaders must also lead by example, cultivating a culture of curiosity and innovation that starts with them.
“When upskilling your workforce for AI, you've got to segment them into different capability levels and meet them where they are.”
Reinvention is no longer optional—and with AI, might finally be possible
The question is no longer whether change is needed in the health and wellbeing sector—but how we can lead it, knowing that traditional solutions are no longer enough. AI now offers a powerful means to address long-standing challenges, improve patient outcomes, and create a more sustainable, equitable system.
However, as our research and roundtables indicate, the journey toward AI adoption is not simple. Concerns around trust and workforce readiness are contributing to sector’s slower adoption of AI.
In this sector, leaders will need to innovate while keeping patient safety front of mind. To successfully harness AI's full potential, health and wellbeing organisations can prioritise the four actions identified through our roundtables:
- Implement strong AI policy and governance frameworks to build trust through the organisation and establish clear guidelines for AI.
- Create an AI ambition by defining a clear AI vision that aligns with the organisation’s strategic goals to ensure AI initiatives drive meaningful business outcomes.
- Evaluate technology and data readiness by investing in scalable enterprise platforms and building strong data foundations to accelerate AI adoption in select use cases.
- Build an AI-enabled workforce by investing in AI literacy and empowering employees to confidently apply AI for meaningful impact.
“If you’re just starting out, one of the best things you could do today is get ‘hands on’ with AI tools yourself. Experiment in a safe environment, be curious, and put the time and energy towards your own upskilling and understanding of the technology.”
PwC and the University of Technology Sydney (UTS) have collaborated to leverage the transformative capabilities of AI in the healthcare sector. Both organisations have a shared vision to make healthcare smarter, faster, and fairer through the integration of AI technologies. By combining PwC's industry expertise with UTS's academic prowess, this collaboration aims to advance AI-driven innovations that lead to improved patient outcomes, enhanced efficiency, and, ultimately, a more equitable healthcare system for Australia.
The future of healthcare is not just about technology—it’s about leadership, curiosity, collaboration, and courage. Are you ready to lead the reinvention?
About the authors
A shared vision for fast, fair and scalable AI
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