Preparing Australian Healthcare Managers for Artificial Intelligence: Current trends and strategic imperatives

Abstract

Objective: To analyse current trends in healthcare Artificial Intelligence (AI) implementation in Australia, identify key management challenges, and provide evidence-based recommendations for preparing healthcare managers for AI integration.

Methods: A comprehensive literature review was conducted using multiple databases, including PubMed and Google Scholar, with search terms such as "Australian healthcare," "artificial intelligence," "management," "implementation," and "strategy." The review focused on publications from 2020 to 2025. Data sources included peer-reviewed articles, policy documents, and implementation frameworks. The inclusion criteria specifically targeted studies that examined the implementation of AI in Australian healthcare contexts, management challenges, and preparation strategies. Data synthesis involved a thematic analysis of identified barriers, enablers, and strategic frameworks.

Results: The review identified limited clinical AI deployment in Australian hospitals, with implementation largely restricted to medical imaging. Digital maturity assessments revealed substantial infrastructure and capability gaps, particularly in predictive analytics. Barriers to adoption included socio-technical resistance, insufficient real-world evidence, fragmented data systems, unclear governance, and funding constraints. Rural services faced additional challenges related to technical support and resource limitations. Evidence from implementation frameworks and competency models highlighted the need for structured governance, capability assessments, and targeted workforce training to close the translational gap between research and practice.

Conclusions: Australian healthcare managers require immediate action through capability assessments, governance establishment, and strategic planning to integrate AI effectively. Medium-term priorities involve silent trials and investment in data infrastructure and competency development. Long-term success is dependent on scaled evaluation, policy advocacy, and sustained leadership commitment to evidence-based adoption of AI. Success will ultimately strike a balance between innovation and safety, efficiency and equity, and technological capability and human-centred care delivery.