AI in Healthcare and Medicine: Applications, Benefits, and Regulation in Australia
Marcie Nguyen
Marcie is a skilled writer at Havi Technology focusing on creating content for marketing, eCommerce, point of sales, and ERP solutions. With over 8 years of experience in the retail, eCommerce and ERP technology sectors, Marcie is dedicated to providing insightful answers to business owners of all scales.
TABLE OF CONTENTS
AI in healthcare and medicine refers to computer technology, including machine learning, that supports how clinicians diagnose, treat, and support patients, and how the health services around them run (Australian Government Department of Health, Disability and Ageing). Adoption in Australia has been driven by workforce shortages, documentation load, and an ageing population, though it has moved unevenly: faster in administrative uses such as ambient scribes, slower in clinical decision-making.
This article covers five practical questions: what AI in healthcare and medicine actually is, and where it is being applied across clinical, operational, and administrative work. It then examines why adoption often stalls at the data and infrastructure layer rather than at the model itself, and what the benefits and risks look like for Australian health services. The final section covers how the technology is regulated across the Therapeutic Goods Administration, the Australian Health Practitioner Regulation Agency, and the Voluntary Code of Conduct on the use of AI in health software.
What is AI in healthcare and medicine?
AI in healthcare and medicine refers to computer systems that use machine learning and related techniques to support clinical decisions, diagnostics, and the administrative work that surrounds patient care. In Australia, it is already being used across cancer screening, ambient scribing during patient consultations, clinical decision support, and monitoring tools that flag patient risk before symptoms appear.
The Australian Health Practitioner Regulation Agency frames it more simply: AI is technology performing tasks that would usually require human intelligence, and in a healthcare setting it is intended to help, not replace, the practitioner. The word "AI" itself covers a range of quite different tools, which is worth spelling out before going further.
The types of AI used in healthcare today
Three types account for most current use. Machine learning, including generative AI, trains models on large datasets to identify patterns or generate text; ambient scribes and clinical decision support tools rest on this. Computer vision reads medical images and sits behind many of the recent gains in radiology, dermatology, and cancer screening. Natural language processing draws structured information out of unstructured clinical notes and is the foundation of most documentation, coding, and search tools currently in use across Australian health services.
The main applications of AI in healthcare and medicine
AI in healthcare and medicine currently spans three broad application areas: clinical work (diagnostics, medical imaging, and clinical decision support), operational work (patient flow, scheduling, and capacity planning), and administrative work (documentation, coding, and billing). The Medical Journal of Australia frames these as a five-phase implementation model, with administrative and operational uses forming the safest early adoption path for most Australian health services (Medical Journal of Australia, 2025).
The three areas, and where each currently sits on the Medical Journal of Australia's phased implementation path, are summarised below.
The three application areas of AI in healthcare and medicine, framed by the Medical Journal of Australia's five-phase implementation model (Scott et al., 2025)
Clinical applications
Clinical applications include medical imaging, skin cancer detection, ECG interpretation, and clinical decision support at the point of care. BreastScreen New South Wales became Australia's first population-based screening program to introduce machine reading of mammograms in November 2025, working alongside its two radiologists (Guardian Australia, 2025). This is where the most visible Australian examples currently sit. Higher-risk uses such as autonomous diagnosis remain rare and heavily supervised.
Operational applications
Operational applications include bed capacity forecasting, patient flow modelling for emergency departments, staffing reallocation, and companion tools for home care clients. These uses are less visible from outside the health service, but they are where much of the current cost-saving actually happens. A consumer-facing example is Aida, the voice bot that St Vincent's Health Australia rolled out to home care clients as a companion tool, checking in on 79-year-old clients like Peta Rolls between visits from carers (Guardian Australia, 2025).
Administrative applications
Administrative applications include ambient AI scribes, automated coding, billing, and discharge summary generation, and are the fastest-growing area of adoption. Ambient AI scribes, which listen to a patient consultation and generate the clinical note in the background, went from 22 per cent to 40 per cent adoption among Australian general practitioners between August 2024 and November 2025 according to a Royal Australian College of General Practitioners poll (Guardian Australia, 2026). The same review places these as Phase 1 and Phase 2 of the five-phase model, because the clinical risk is lower and the workflow gain is direct.
Where AI in healthcare stalls: the data and infrastructure bottleneck
AI adoption in Australian healthcare is limited less by model capability and more by data infrastructure, workflow integration, and governance. Digital Health CRC chief executive Annette Schmiede has described the central barriers as data infrastructure, standards, evidence base, and community trust, rather than the AI technology itself (Medical Republic, 2026). The same framing runs across Australian Productivity Commission reporting and the Australian Alliance for AI in Healthcare's work on sovereign capability.
The three real bottlenecks
The three real bottlenecks are data infrastructure, workflow integration, and governance. Data infrastructure is the first ceiling: Australian health data is scattered across My Health Record, hospital electronic medical records, general practice systems, and specialist platforms, most of which were built long before AI adoption was on the roadmap. Workflow integration is the second: even where useful AI tools exist, plugging them into legacy clinical workflows is where projects tend to stall. Governance is the third: policy is currently distributed across the Therapeutic Goods Administration, the Australian Health Practitioner Regulation Agency, and the Office of the Australian Information Commissioner, with visible gaps between them.
Why sovereign AI capability matters for Australian health services
Sovereign AI capability matters because Australia needs domestic capability in the model layer, the data layer, and the standards layer to keep control over how its healthcare system evolves. Professor Enrico Coiera, chair of the Australian Alliance for AI in Healthcare, has argued that if generative AI becomes embedded in the foundations of Australian healthcare and those foundations are owned overseas, the country becomes "hostage to geopolitics" (Medical Republic, 2026). His practical point is not that overseas AI is unusable. The Alliance's workshops and the Digital Health CRC's programme of work are attempting to build that capability.
What "large islands of disconnected data" means in practice
It looks something like this:
Australian health data as "large islands of disconnected data", the concept Dr Paul Cooper describes as the ceiling on agentic AI adoption (PulseIT, 2025)
In practice, "large islands of disconnected data" means that even where useful AI tools exist, they cannot carry actions from one system to the next because the underlying systems do not talk to each other. Dr Paul Cooper, writing in PulseIT, framed it as intake and discharge remaining classic administrative bottlenecks (PulseIT, 2025). Agentic AI, the kind that can carry an action across systems rather than just answering a single question, only works when the underlying systems are integrated enough to hand data cleanly between them. That is the layer the Australian Productivity Commission's December 2025 report, Delivering Quality Care More Efficiently, pointed at when it flagged interoperable data as the base of any productivity gain (Medical Republic, 2026).
Benefits and risks of AI in healthcare
AI in healthcare offers measurable gains in clinician time, documentation accuracy, and early detection, but carries real risks around bias, hallucination, and data privacy. Australian data from the Medical Journal of Australia shows ambient AI scribes can reduce documentation time by up to 25 per cent, while self-reported clinician burnout dropped from 52 per cent to 39 per cent after 30 days of scribe use (Medical Journal of Australia, 2025; MJA InsightPlus, 2026). The same sources flag reliability, algorithmic bias, and consent gaps as ongoing concerns.
Benefits observed in Australian settings
Risks flagged by Australian regulators and clinicians
Documentation time reduction of up to 25 per cent (Medical Journal of Australia, 2025)
Reliability and hallucination in generative outputs
Clinician burnout drop from 52 per cent to 39 per cent after 30 days on ambient scribes (MJA InsightPlus, 2026)
Algorithmic bias when training data lacks Australian demographic diversity
Faster and more sensitive image reading in screening programs (BreastScreen New South Wales)
Data sovereignty gaps, including cross-border data flow (Guardian Australia, July 2026)
Higher-accuracy discharge summaries, ahead of junior doctors in 90 per cent of trial cases (Medical Journal of Australia, 2025)
Consent gaps in scribe deployment and unclear patient awareness
The measurable benefits emerging in Australian settings
The measurable benefits emerging in Australian settings sit in three areas: documentation time, clinician burnout, and imaging turnaround. The strongest evidence is in documentation. A November 2025 narrative review in the Medical Journal of Australia described ambient scribing as "keyboard liberation" because the time reclaimed is significant and immediate; hospital residents and nurses were previously spending up to 25 per cent and 60 per cent of shift time on documentation, respectively (Medical Journal of Australia, 2025). Burnout numbers have shifted with it, and imaging and screening evidence shows early gains in sensitivity and turnaround for AI-assisted reads.
The risks Australian regulators and clinicians are actively warning about
The risks being actively raised sit in four areas: data sovereignty, algorithmic bias, hallucination, and consent gaps. The federal health department warned in February 2026 Senate estimates that some scribe suppliers may be unaware their cloud platforms send patient data outside Australia (Guardian Australia, 2026). Anja Nikolic, chief executive of the Australian Institute of Digital Health, has been consistent that a "human in the loop" is not optional for high-risk clinical uses. Bias in models trained on non-Australian populations, hallucinated content presented with confidence, and gaps in patient consent are all being actively raised by clinicians and regulators.
How AI in healthcare is regulated in Australia
In Australia, AI used in healthcare sits under a layered regulatory framework. The Therapeutic Goods Administration regulates AI tools that meet the definition of a medical device, known as Software as a Medical Device. The Australian Health Practitioner Regulation Agency sets the professional obligations that apply when practitioners use AI in clinical work. The Voluntary Code of Conduct on the use of AI in health software, first signed by Magentus in February 2026, covers governance areas that sit outside the Therapeutic Goods Administration's remit.
The layered regulatory frame governing AI in healthcare and medicine in Australia, showing the coverage of the Therapeutic Goods Administration, the Australian Health Practitioner Regulation Agency, and the Voluntary Code of Conduct on AI in health software, plus the governance gap between them.
Illustrative summary only. For current requirements, consult the Therapeutic Goods Administration, Ahpra, and the Voluntary Code of Conduct directly.
What the Therapeutic Goods Administration regulates (and what it does not)
The Therapeutic Goods Administration regulates AI tools that meet the definition of a medical device, that is, tools with a therapeutic purpose such as diagnosis, treatment, or monitoring. General-purpose tools such as ambient GenAI scribes fall outside that definition because they only assist with documentation. A Therapeutic Goods Administration review of digital scribes is currently underway, referenced during February 2026 Senate estimates (Guardian Australia, 2026).
What the Australian Health Practitioner Regulation Agency expects of practitioners using AI
The Australian Health Practitioner Regulation Agency's "Meeting your professional obligations when using Artificial Intelligence in healthcare" guidance sets out five key principles for practitioners using AI in practice: accountability, understanding, transparency, informed consent, and ethical and legal considerations (Australian Health Practitioner Regulation Agency). In plain terms, the practitioner remains responsible for the clinical decision, must understand the tool they are using, must inform patients about how AI is being applied, must obtain appropriate consent, and must comply with existing privacy and professional obligations.
The Voluntary Code of Conduct and what it fills in
The Voluntary Code of Conduct on the use of AI in health software covers ten governance domains, including accountability, risk management, data governance, and transparency. It was developed by the Medical Software Industry Association and the Medical Technology Association of Australia, with Magentus signing as the first vendor in February 2026 (IT Brief AU, 2026). A formal review is scheduled for mid-2026. The Code is not enforceable law. It is a mechanism for the industry to hold itself to standards the Therapeutic Goods Administration does not currently cover.
Key Takeaways
For readers with more specific questions, the answers below cover what tends to come up most often when Australian healthcare organisations start looking at AI in practice.
Frequently asked questions about AI in healthcare and medicine
What is the difference between clinical AI and operational AI in healthcare?
Clinical AI supports diagnostic and treatment decisions (imaging, decision support). Operational and administrative AI supports the work around care (scheduling, documentation, billing). The distinction matters because risk tiers, regulation, and adoption paths differ, and the Medical Journal of Australia's five-phase model places administrative uses first for that reason (Medical Journal of Australia, 2025). Outside healthcare, the same operational category (scheduling, documentation, billing, and cross-departmental coordination) sits at the heart of AI in ERP systems, which examines how AI capabilities are being embedded into finance, supply chain, and inventory workflows to reduce manual handoffs and shorten decision cycles.
Is AI replacing doctors in Australia?
No. The Australian Health Practitioner Regulation Agency's position is that AI assists rather than replaces the clinician, and accountability for the clinical decision stays with the practitioner. Anja Nikolic, chief executive of the Australian Institute of Digital Health, has been consistent that "human in the loop" is the operating principle across Australian regulatory guidance.
How much time can AI save Australian clinicians on documentation?
Up to 25 per cent. Ambient AI scribes can reduce documentation time by around that much (Medical Journal of Australia, 2025). The baseline is higher than most people expect: Australian clinicians in some settings spend up to two hours on documentation for every hour of direct patient interaction.
What is an AI scribe and how widely is it used in Australia?
An AI scribe is a generative AI tool that listens to a patient consultation and drafts the clinical note in the background. Use among Australian general practitioners rose from 22 per cent in August 2024 to 40 per cent in November 2025, according to a Royal Australian College of General Practitioners poll (Guardian Australia, 2026).
Can patient data be sent overseas by AI healthcare tools?
In some cases, yes. The federal health department warned during February 2026 Senate estimates that some scribe suppliers may be unaware that their cloud platforms send patient data outside Australia (Guardian Australia, 2026). Data sovereignty is one of the five pre-adoption checks in the Medical Journal of Australia InsightPlus guidance for general practitioners.
What role does the Therapeutic Goods Administration play in regulating AI medical software?
The Therapeutic Goods Administration regulates AI tools meeting the definition of a medical device, that is, tools with a therapeutic purpose such as diagnosis or treatment. General-purpose tools like ambient scribes are not currently regulated by the Therapeutic Goods Administration because they only assist with documentation. A review of digital scribes is underway.
What Australian healthcare organisations should do about AI right now
For an Australian healthcare organisation weighing all of this, the practical starting point is not the AI model. The Medical Journal of Australia InsightPlus pre-adoption guidance walks through five checks worth doing first: verify Therapeutic Goods Administration status of any tool being considered, confirm where patient data actually flows and whether that meets Australian residency expectations, establish informed consent protocols, draft an AI policy the whole team can point to, and train the team in how the tool should and should not be used (MJA InsightPlus, August 2026). Dr Brent Richards, at Griffith University, has argued that a general practice is a business like any other, and that the practical starting point when adopting AI is to look at the back-office work first, where risk is lowest, and workflow gain is direct. That is the same reasoning behind the operational-lens throughline across this article: data infrastructure, workflow integration, and governance are the foundations AI adoption depends on.
Havi works with Australian organisations on the operational systems layer that AI adoption depends on, including ERP integration, workflow automation, and data governance.
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