Generative artificial intelligence and non-pharmacological bias: an experimental study on cancer patient sexual health communications

Author:

Hanai AkikoORCID,Ishikawa Tetsuo,Kawauchi Shoichiro,Iida Yuta,Kawakami Eiryo

Abstract

ObjectivesThe objective of this study was to explore the feature of generative artificial intelligence (AI) in asking sexual health among cancer survivors, which are often challenging for patients to discuss.MethodsWe employed the Generative Pre-trained Transformer-3.5 (GPT) as the generative AI platform and used DocsBot for citation retrieval (June 2023). A structured prompt was devised to generate 100 questions from the AI, based on epidemiological survey data regarding sexual difficulties among cancer survivors. These questions were submitted to Bot1 (standard GPT) and Bot2 (sourced from two clinical guidelines).ResultsNo censorship of sexual expressions or medical terms occurred. Despite the lack of reflection on guideline recommendations, ‘consultation’ was significantly more prevalent in both bots’ responses compared with pharmacological interventions, with ORs of 47.3 (p<0.001) in Bot1 and 97.2 (p<0.001) in Bot2.DiscussionGenerative AI can serve to provide health information on sensitive topics such as sexual health, despite the potential for policy-restricted content. Responses were biased towards non-pharmacological interventions, which is probably due to a GPT model designed with the ’s prohibition policy on replying to medical topics. This shift warrants attention as it could potentially trigger patients’ expectations for non-pharmacological interventions.

Funder

RIKEN

Publisher

BMJ

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