ArticleThe Australasian journal of dermatology2025
Attitudes Towards the Use of Artificial Intelligence in Dermatology: A Survey of Australian Dermatologists.
Article in The Australasian journal of dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Implementation of Total Body Photography for Skin Cancer Early Detection: A Systematic Review.International journal of dermatology · 2026Pooled it
- The Utility of Artificial Intelligence in Dermatology Training and Practice: A National, Cross-Sectional Study.The Australasian journal of dermatology · 2026Article
- Perspectives on Artificial Intelligence in Dermatology: An International Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2026Article
- The double-edged sword of generative AI in dermatology: a multi-component cross-sectional study on physician burnout, patient satisfaction, and communication quality.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
BACKGROUND/
objectivesThis study explored the views of dermatologists in Australia on the use of Artificial Intelligence (AI) in dermatology.
methodFellows and Trainees of the Australasian College of Dermatologists (ACD) were invited to participate in an anonymous online survey, resulting in a sample of 122 completed surveys (response rate 16.2%).
resultsAlthough 44% have used AI in their dermatology practice, only a minority are using AI regularly for clinical (12%) or administrative purposes (17%). A key barrier is trust, with most (69%) participants either unwilling or unsure about trusting AI for supporting the diagnosis of skin cancer. Participants identified accuracy and information on datasets, limitations, benefits, and the purpose of AI as important for trust. 52% want AI accuracy to be equivalent to or superior to the best dermatologist if it is used for clinical diagnosis. Participants perceived a range of benefits and risks of AI, with key risks around accuracy and divestment of AI to tech companies, and key benefits being reduction of monotonous tasks and improved patient access. Few (10%) are concerned about AI replacing dermatologists even though almost half (47%) expect key aspects of dermatology work will be performed by AI.
conclusionsDermatologists in Australia are at an early stage of integrating AI into practice, with most wary or unsure about the accuracy of currently available AI tools for diagnostic purposes. Developing workflows that are acceptable to clinicians may require knowledge outreach from Dermatology Colleges, such as the ACD, to help clinicians develop well informed views on AI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.