User Perceptions of Trust in Generative AI for Healthcare Advice

Sammanfattning

With ongoing strains and disparities in the healthcare system, Generative AI applications are increasingly viewed as promising tools to alleviate pressure in this sector. Trust is seen as a critical enabler for the integration of GenAI tools. However, this area remains largely unexplored, with limited research examining user-perceived trust. This study addresses this gap by adopting a qualitative approach to explore user perceptions of trust in GenAI tools, specifically text-based LLMs, for providing healthcare advice. Drawing on semi-structured interviews with participants aged 18–34, this study uncovers the complexities of trust. Find-ings suggest that user-perceived trust is shaped by the systems attributes like transparency, explainability and responsiveness, as well as individual aspects such as prior experience, perceived control, and the nature of the health concern. While participants generally viewed LLM-based tools as accessible and useful, their trust was dynamic, influenced by context, familiarity, and the perceived reliability of the tool. These findings highlight the importance of educating users, transparent AI behaviour, and responsible integration into healthcare. As the utilisation of LLMs and GenAI continue to increase in sensitive domains, it becomes increasingly important to continue researching the subject of trust, to ensure a safe and controlled integration of these applications.