ArticleThe Australasian journal of dermatology2026
The Utility of Artificial Intelligence in Dermatology Training and Practice: A National, Cross-Sectional Study.
Article in The Australasian journal of dermatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND/
objectivesArtificial intelligence (AI) is increasingly relevant to dermatology, yet clinical integration depends on workforce readiness. While the technical performance of AI tools is well described, the perspectives of dermatology trainees, who will shape future adoption, are less well understood. The objective of this study was to investigate Australian dermatology trainees' knowledge, utilisation, and perceptions of AI, and to identify barriers to implementation.
methodsA national, cross-sectional electronic survey was distributed to all Australian dermatology trainees (n = 118) enrolled with the Australasian College of Dermatologists between February 2025 and June 2025. Outcomes included self-reported familiarity with and use of AI tools, perceived utility across clinical and non-clinical tasks, and perceived barriers to integration.
resultsSixty-eight trainees responded (57.6%). Most trainees (81.4%) agreed that AI is likely to become an important tool in dermatology over the next 5-10 years. However, 69.1% reported no formal training. 32.4% had used AI tools, most commonly general-purpose generative AI, with use primarily informal and focused on educational, research, and administrative tasks rather than direct patient care. Commonly reported barriers included legal and ethical considerations (60.3%), concerns regarding reliability (54.4%), and limited training or knowledge (52.9%).
conclusionsAustralian dermatology trainees express cautious optimism about AI, recognising its potential while identifying practical, educational, and governance-related challenges. Current use is limited and largely non-clinical, reflecting early-stage adoption. These findings highlight opportunities for structured AI literacy and education to support future integration as evidence, governance frameworks, and clinical applications continue to evolve.
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