Trust in large language model-based solutions in healthcare among people with and without diabetes: a cross-sectional survey from the Health in Central Denmark cohort.

OBJECTIVE: Large language models have gained significant public awareness since ChatGPT's release in 2022. This study describes the perception of chatbots in healthcare among people with and without diabetes. METHODS AND ANALYSIS: In 2024, an online survey was sent to 136 229 people, aged 18-89 years in the Health in Central Denmark cohort, including equestions related to the perception of artificial intelligence (AI) and chatbots. Questions assessed trust in chatbots in various healthcare scenarios (lifestyle, diagnostic, contact with general practitioner (GP) and emergency contact) alongside participants' level of experience with ChatGPT. In one item, participants were randomly presented with either a more severe (emergency) or less severe (GP contact) scenario. We used multinomial logistic regression to investigate the association of diabetes status and demographic characteristics with trust in chatbots in different scenarios. RESULTS: 39 109 participants completed the questionnaire. The majority were aware of AI (94%), although fewer had heard of ChatGPT (76%), and only 21% had tried it. Most participants trusted chatbots with involvement of healthcare professionals (HCPs) (49%-55%), while few trusted without them (3%-6%). The degree of trust depended on the severity of the scenario, demonstrated by lower odds (OR 0.63, 95% CI 0.60 to 0.66) of trusting the chatbot under the control of HCPs in emergency care compared with contact with the GP. Type 2 diabetes, but not type 1 diabetes, was associated with less trust in chatbots than people without diabetes. Moreover, age, sex, duration of education and experience with ChatGPT also had an impact on trust. CONCLUSION: Chatbots are seen as supportive tools among public users when controlled by HCPs but are met with more scepticism in more severe situations. Digital exclusion risks and demographic differences, such as age, sex and disease-specific conditions (eg, type 2 diabetes), need to be addressed to ensure equitable and meaningful implementation.