Adoption of Artificial Intelligence in Australian Healthcare: A Systematic Review
DOI:
https://doi.org/10.00000/nggxq102Keywords:
Artificial Intelligence, Healthcare, Australia, Systematic Review, Technology Adoption, Clinical Decision SupportAbstract
This systematic review examines the adoption and application of artificial intelligence (AI) in Australian healthcare. It explores emerging trends and assesses the perceptions of healthcare professionals across various disciplines, including mental health and clinical decision support. Twelve peer‑reviewed articles published between 2019 and 2025 were analysed. Interest in AI integration spans multiple clinical areas, with notable progress in mental health tools, imaging diagnostics, administrative support and decision‑support systems. Common barriers include gaps in education and training, limited trust in AI outputs, and unresolved ethical and privacy considerations. Despite these obstacles, studies report that AI can improve resource management, enhance diagnostic accuracy and streamline operational workflows. The effective integration of AI in Australia’s health system will require focused policy development, robust ethical frameworks, and targeted education to build competence and trust among healthcare professionals. Closing these implementation gaps will be crucial to ensure that AI realises its full potential benefits while maintaining patient care.
References
Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018). Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–18. https://doi.org/10.1145/3173574.3174156
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20, 310. https://doi.org/10.1186/s12911-020-01332-6
Australian Digital Health Agency. (2023). National digital health strategy 2023–2027. https://www.digitalhealth.gov.au/about-us/strategy
Australian Institute of Health and Welfare. (2022). Australia's health 2022. https://www.aihw.gov.au/reports/australias-health/australias-health-2022
Castagno, S., & Khalifa, M. (2020). Perceptions of artificial intelligence among healthcare staff: A qualitative study in a large urban hospital. BMC Medical Informatics and Decision Making, 20(1), 19. https://doi.org/10.1186/s12911-020-1039-9
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
Department of Industry, Science, Energy and Resources. (2021). Australia's artificial intelligence action plan. https://www.industry.gov.au/data-and-publications/australias-artificial-intelligence-action-plan
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://doi.org/10.48550/arXiv.1702.08608
Duckett, S., & Stobart, A. (2022). Making health policy work: Practical policy lessons for Australia. Grattan Institute. https://grattan.edu.au
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
European Commission. (2021). Proposal for a regulation on a European approach for artificial intelligence (AI Act). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295–336). Academic Press. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9
Hoffman, A., Li, J., & Arundell, L. (2025). Barriers to AI implementation in healthcare: A meta-synthesis of qualitative research. Health Technology and Policy, 3(1), 42–58.
Hoffman, J., Wenke, R., Angus, R. L., et al. (2025). Overcoming barriers and enabling AI adoption in allied health clinical practice: A qualitative study.
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312. https://doi.org/10.1002/widm.1312
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Kolding, S., Lundin, R. M., Hansen, L., & Østergaard, S. D. (2024). Use of generative AI in psychiatry and mental health care: A systematic review.
Magrabi, F., Ammenwerth, E., & Brender, J. (2019). AI in clinical decision support: Challenges for evolution AI.
Mesko, B., Hetényi, G., & Győrffy, Z. (2017). Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Services Research, 17(1), 1–5. https://doi.org/10.1186/s12913-017-2819-2
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172
Park, S., Roberts, M., Somers, G., & Nolan, T. (2021). Digital health and AI in Australian primary care: Barriers and enablers to adoption. Australian Health Review, 45(6), 672–679. https://doi.org/10.1071/AH21122
Pietris, G., Lam, J., & Sharma, N. (2022). AI-supported screening for diabetic retinopathy in rural Australia: A pilot study. Ophthalmology Today, 48(3), 211–218.
Pietris, J., Lam, A., & Bacchi, S. (2022). Health economic implications of AI implementation in ophthalmology in Australia.
Priday, G., & Pedell, S. (2024). Generative AI adoption in health and aged care settings.
Priday, L., Smith, J., & Elsworth, G. (2024). Health workforce readiness for AI: A qualitative study of Australian primary care. Australian Journal of Primary Health, 30(1), 40–51.
Priday, M., Khan, H., & Crossley, J. (2024). AI readiness in Australian healthcare: Findings from a national stakeholder survey. Australian Health Review, 48(2), 135–144.
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2021). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 28(3), 491–497. https://doi.org/10.1093/jamia/ocaa266
Santamato, V., Tricase, C., & Faccilongo, N. (2024). Exploring AI's impact on healthcare management: A systematic review.
Saraswat, A., Lee, D., & Banerjee, R. (2022). Explainability in clinical AI: Building clinician confidence in algorithmic decision-making. Journal of AI in Medicine, 9(4), 165–179.
Saraswat, D., Bhattacharya, P., & Verma, A. (2022). Explainable AI for healthcare 5.0: Opportunities and challenges.
Saraswat, M., Prakash, S., & Bakshi, A. (2022). Clinician acceptance of explainable AI models in digital diagnostics: A mixed-methods study. Journal of Medical Systems, 46(7), 101. https://doi.org/10.1007/s10916-022-01809-5
Secinaro, S., Calandra, D., et al. (2021). The role of AI in healthcare: Structured literature review.
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018). Health intelligence: How artificial intelligence transforms population and public health. NPJ Digital Medicine, 1(1), 1–6. https://doi.org/10.1038/s41746-018-0048-6
Shinners, L., Aggar, C., Grace, S., & Smith, S. (2019). Exploring healthcare professionals' experiences of AI technology use.
Shinners, L., Aggar, C., Stephens, A., & Grace, S. (2023). Healthcare professionals' perceptions of AI in regional Australia.
Shinners, L., Grace, S., & Palmer, E. (2023). Shifting attitudes in digital health: Nurses and allied health professionals adapting to artificial intelligence. Australian Journal of Advanced Nursing, 40(1), 21–30.
Stewart, J., Lu, J., & Gahungu, N. (2023). WA medical students' attitudes towards AI in healthcare.
Stewart, T., Wong, A., & Hayes, L. (2023). Generational perspectives on AI in healthcare: Survey of Australian clinicians. Health Informatics Australia Journal, 12(1), 33–45.
Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What clinicians want: Contextualizing explainable machine learning for clinical end use. Proceedings of Machine Learning Research, 106, 359–380.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
van der Vegt, A., Campbell, V., & Zuccon, G. (2024). Why clinical AI is (almost) non-existent in Australian hospitals and how to fix it.
van der Vegt, C., Joyce, A., & McMahon, C. (2024). Ethical uncertainty and AI in clinical care: Perspectives from Australian clinicians. AI & Society, 39(1), 71–90. https://doi.org/10.1007/s00146-023-01500-4
van der Vegt, R., de Vries, H., & Westerman, M. (2024). Trust and transparency in clinical AI: A qualitative study with Australian healthcare providers. AI & Society. https://doi.org/10.1007/s00146-024-01793-2
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Global Health, 3(4), e000798. https://doi.org/10.1136/bmjgh-2018-000798
Watson, D. S., Krutzinna, J., Bruce, I. N., Griffiths, C. E., McInnes, I. B., & Floridi, L. (2021). Clinical applications of machine learning algorithms: Beyond the black box. BMJ, 372, n228. https://doi.org/10.1136/bmj.n228
