Sexual health service users’ perspectives on artificial intelligence applications for identification of lesions associated with sexually transmissible infections: A qualitative study - Alicia J King; Nyi Nyi Soe; Phyu Mon Latt; Lei Zhang; Meredith Temple-Smith; Kate Maddaford; Christopher K Fairley; Eric PF Chow; Tiffany R Phillips (Digital Health)
Background: Early identification of sexually transmissible infections (STIs) is central to their control by facilitating timely access to healthcare. A web-based sexual health application using artificial intelligence (AI) to identify the risk of a genital or anal lesion being an STI, from a photographic image, is in development. However, the usefulness, accessibility, and acceptability of this technology to potential users are unknown. Method: This research adopted a developmental evaluation approach to explore potential users’ views of the usefulness of this AI application, as well as factors impacting its acceptability and accessibility. Three focus groups were conducted with attendees of a sexual health clinic. Focus group discussions were recorded, transcribed, and analysed using action coding to identify key concepts. Results: A diverse group of 12 participants provided feedback on the proposed application. Feedback on the application's usefulness highlighted its potential value in supporting access to individualised sexual health information; informing decisions about healthcare seeking; and preventing transmission to others. Factors mediating the accessibility and acceptability of AI-powered applications included potential users’ awareness of the application; concerns about data security; the accessibility of the application to diverse user groups; trust in the technology; and transparency about the application's limitations. Conclusion: AI applications for the identification of visible changes in the ano-genital region have the potential to support access to sexual health information, healthcare seeking and the prevention of onward transmission of STIs. Codesign approaches will increase the likelihood of more widespread uptake and public health impact of AI-powered sexual health applications.