Evidence map›Paper›PMID 40679180›Full record

ReviewThe Australasian journal of dermatology2025

Informing a Position Statement on the Use of Large Language Models and AI Scribes in Dermatology in Australia.

Liam J Caffery, Monica L Taylor, Lisa M Abbott, Monika Janda, Pascale Guitera, Victoria Mar, Chris Arnold, Stephen Shumack, Tony Caccetta, Robert Miller and 1 more

Abstract readReviewConsensus Statement
PubMed Full text at PMC Publisher
In one paragraph

Review 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Liam J CafferyCentre for Online Health, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0003-1899-7534
Monica L TaylorCentre for Online Health, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0001-5333-2955
Lisa M AbbottThe Australasian College of Dermatologists, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-4005-8421
Monika JandaCentre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-1728-8085
Pascale GuiteraFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-9519-110X
Victoria MarThe Australasian College of Dermatologists, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-9423-3435
Chris ArnoldThe Australasian College of Dermatologists, Sydney, New South Wales, Australia.
Stephen ShumackThe Australasian College of Dermatologists, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-9121-7795
Tony CaccettaThe Australasian College of Dermatologists, Sydney, New South Wales, Australia.
Robert MillerThe Australasian College of Dermatologists, Sydney, New South Wales, Australia.
H Peter SoyerDermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-4770-561X

Funding

Australasian College of Dermatologists
6 · The paper itself

Abstract

Artificial Intelligence (AI) refers to the ability of computers to mimic human intelligence. In response to the growing interest and impact of AI, the Australasian College of Dermatologists released its first Position Statement on AI in dermatology in 2022. This Position Statement provided guidance for dermatologists on the appropriate use of AI. Since then, the AI landscape has evolved substantially, particularly with the emergence of Large Language Models (LLMs). This article explores key developments in AI driven by LLMs, including the increasing use of AI scribes, and provides updated guidance for dermatologists in Australia.

Indexed as

Artificial IntelligenceDermatologyDocumentationLanguageAustraliaHumansLarge Language Modelsartificial intelligencedermatologyguidelineslarge language modelsmachine learningposition statement

Identifiers

PMID40679180
PMCPMC12418137

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.