Evidence map›Paper›PMID 39485672›Full record

ArticleThe patient2025

What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment.

Nyi Nyi Soe, Phyu Mon Latt, Alicia King, David Lee, Tiffany R Phillips, Christopher K Fairley, Lei Zhang, Jason J Ong

Abstract read
In one paragraph

Article in The patient, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. 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

8 authors.

Nyi Nyi SoeArtificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia. drnyinyisoe1989@gmail.com.
Phyu Mon LattArtificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Alicia KingSchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
David LeeMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.
Tiffany R PhillipsSchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
Christopher K FairleySchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
Lei Zhang *Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia. lei.zhang1@monash.edu.
Jason J Ong *School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. Jason.ong@monash.edu.

Funding

National Health and Medical Research Council GNT1172900National Health and Medical Research Council GNT1193955
6 · The paper itself

Abstract

backgroundOne of the World Health Organization (WHO) recommendations to achieve its global targets for sexually transmitted infections (STIs) is the increased use of digital technologies. Melbourne Sexual Health Centre (MSHC) has developed an AI-assisted screening application (app) called AiSTi for the detection of common STI-related anogenital skin conditions. This study aims to understand the community's preference for using the AiSTi app.

methodsWe used a discrete choice experiment (DCE) to understand community preferences regarding the attributes of the AiSTi app for checking anogenital skin lesions. The DCE design included the attributes: data type; AI accuracy; verification of result by clinician; details of result; speed; professional support; and cost. The anonymous DCE survey was distributed to clients attending MSHC and through social media channels in Australia between January and March 2024. Participant preferences on various app attributes were examined using random parameters logit (RPL) and latent class analysis (LCA) models.

resultsThe median age of 411 participants was 32 years (interquartile range 26-40 years), with 64% assigned male at birth. Of the participants, 177 (43.1%) identified as same-sex attracted and 137 (33.3%) as heterosexual. In the RPL model, the most influential attribute was the cost of using the app (24.1%), followed by the clinician's verification of results (20.4%), the AI accuracy (19.5%) and the speed of receiving the result (19.1%). The LCA identified two distinct groups: 'all-rounders' (88%), who considered every attribute as important, and a 'cost-focussed' group (12%), who mainly focussed on the price. On the basis of the currently available app attributes, the predicted uptake was 72%. In the short term, a more feasible scenario of improving AI accuracy to 80-89% with clinician verification at a $5 cost could increase uptake to 90%. A long-term optimistic scenario with AI accuracy over 95%, no clinician verification and no cost could increase it to 95%.

conclusionsPreferences for an AI-assisted screening app targeting STI-related anogenital skin lesions are one that is low-cost, clinician-verified, highly accurate and provides results rapidly. An app with these key qualities would substantially improve user uptake.

Indexed as

Mass ScreeningMobile ApplicationsPatient PreferenceSexually Transmitted DiseasesAdultAustraliaChoice BehaviorFemaleHumansMaleSurveys and Questionnaires

Identifiers

PMID39485672
PMCPMC11832619

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.