Global analysis of country-level factors associated with chatbot usage for health

Health queries are among the most common uses of consumer conversational artificial intelligence (AI), yet little is known about how this usage varies across countries and what characteristics are associated with it. Here we analysed 1.7 million de-identified health-related conversations from Microsoft Copilot across 109 countries and regions, examining two dimensions: intensity (the share of all conversations that are health-related) and composition (the distribution across eight user-intent categories). We find that these dimensions have largely non-overlapping predictors. A consistent development gradient is associated with a shift in the query mix from broad health informational intents to specific clinical and system-navigation intents in wealthier, older-population countries, while lower population-level confidence in hospitals was the strongest predictor of health conversation intensity (r = −0.41, P < 0.001), though this association is more conditional on specification. These patterns suggest that conversational AI usage is associated with the structure and perceived quality of national health ecosystems. Moreover, these findings present additional perspectives as to how conversational AI systems can be leveraged to better meet specific population needs and augment their health and wellbeing. An analysis of 1.7 million health-related conversations with Microsoft Copilot in 109 countries reveals heterogeneous patterns in usage and type of query, varying with respect to income levels, age structure and trust in healthcare and governmental systems.