AI-Assisted Imaging for Suspected Adolescent Endometriosis: Perspectives from Qualitative Focus Groups

Background: Artificial intelligence (AI)-assisted imaging may support recognition of endometriosis, but perspectives on its use in adolescent care remain unclear. This study explored young people, parents, and health professionals’ views on its potential benefits, acceptable roles, perceived risks, and safeguards. Methods: Four semi-structured focus groups were conducted with adolescents aged 14–17 years (n = 3), young adults aged 18–25 years (n = 9), parents or carers (n = 5), and health professionals (n = 13). Data were analyzed using reflexive thematic analysis. Results: Four themes were developed: (1) diagnostic support, (2) human–AI collaboration, (3) automation bias and diagnostic harm, and (4) responsible AI use. Participants were cautiously hopeful that AI could improve image quality, identify subtle or missed findings, and reduce interpretive variation, but supported it only as an adjunct to skilled clinical care. They feared that apparently objective negative outputs could create false reassurance, reinforce diagnostic dismissal, and restrict further investigation. Trust depended on adolescent-relevant validation, representative data, transparent communication and consent, clear accountability, and clinicians retaining the expertise and authority to question or override AI outputs. Conclusion: AI-assisted imaging should strengthen rather than replace clinical judgment, and a negative AI-assisted imaging result should not prevent further assessment when symptoms persist.