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
Authors: Schaarup Jonas R. , Isaksen Anders Aasted , Norman Kasper , Bjerg Lasse , Hulman Adam 
Published: February 2025

Background

Large language models have gained significant public awareness since ChatGPT’s release in 2022. This study describes the perception of chatbot-assisted healthcare among people with and without diabetes.

Methods

In 2024, an online survey was sent to 136,229 people, aged 18–89 years in the Health in Central Denmark cohort, including eight questions related to the perception of 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.

Findings

39,109 participants completed the questionnaire. The majority were aware of AI (94%), though fewer had heard of ChatGPT (76%), and only 21% had tried it. Most participants trusted chatbots with involvement of healthcare professionals (HCP) (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: 0.66]) of trusting the chatbot under the control of HCP in emergency care compared to contact with the general practitioner. Type 2 diabetes but not type 1 diabetes was associated with less trust in chatbots than people without diabetes. Moreover, age, sex, education, and experience with ChatGPT also had an impact on trust.

Interpretation

Chatbots are seen as supportive tools among public users when controlled by HCPs but are met with more skepticism in more severe situations. Digital exclusion risks and demographic differences, such as age, sex, and disease-specific conditions (e.g., type 2 diabetes) needs, must be addressed to ensure equitable and meaningful implementation.

Research in Context

Evidence before this study

Earlier studies have highlighted the generally positive attitudes of patients and the public towards the applications of artificial intelligence (AI) in healthcare. However, it noted a lack of clear characteristics associated with the acceptance of AI, with many patients preferring AI solutions to remain under human supervision rather than fully replacing healthcare professionals (HCPs). Since ChatGPT emerged in 2022, AI tools have been widely available to the general public, and many healthcare-specific chatbots are now being evaluated in random control trails. Some patients are already turning to tools like ChatGPT for medical advice, further underscoring the need to understand user perceptions, particularly in relation to diabetes and other characteristics, as these technologies may become integrated into care. Our earlier study showed that among AI applications, chatbots were the most controversial when used in emergency care. Thus, understanding the perception of chatbots in different healthcare contexts is needed, as the level of controversy may depend on their specific role in healthcare.

Added value of this study

Our study expands on previous work by engaging a larger cohort of 39,109 participants, which includes a comprehensive representation of older adults and individuals with and without diabetes. Our survey was conducted between February-May 2024, a time when ChatGPT had been accessible for more than 1 year. We assessed trust in chatbot-based healthcare solutions, revealing that, while the majority accepted chatbot assistance under human control, individuals with type 2 diabetes exhibited less trust in such applications compared to those without diabetes or type 1 diabetes. Our findings underscore that the severity and acuteness of healthcare scenarios influenced trust levels.

Implications of all available evidence

Our findings suggest that while AI and chatbots are becoming widely available, uncertainty about their benefits and risks in healthcare persists. People view healthcare professionals as playing an important role in supporting them, particularly in severe scenarios, toward adopting chatbot solutions. A patient-centered approach is necessary, with tailored solutions to address variations in trust based on factors such as diabetes status, age, sex, and education. Ensuring the involvement of vulnerable populations, such as the elderly and those with type 2 diabetes, is key to avoiding digital exclusion and making chatbot solutions accessible and meaningful.