Presentation (CTI)

OBJECTIVES: Artificial intelligence (AI) chatbots have the potential to revolutionize healthcare delivery by offering accessible, scalable, and personalized support for health treatments. These technologies are particularly valuable in addressing healthcare disparities in resource-constrained settings, including developing countries. This study develops an extended technology adoption framework, building on models from the literature, to explore the factors influencing the adoption of AI chatbots for supporting health treatments.
METHODS: The study surveyed 430 Brazilian adults, utilizing a questionnaire consisting of established scales in health technology research. Constructs included Behavioral Intention, Attitude, Perceived Ease of Use, Empowerment, Perceived Knowledge, and Trust. Respondents had a mean age of 43 years, with 57% identifying as female. A total of 52.5% of the participants reported awareness of AI chatbot applications in healthcare.
RESULTS: The analysis uncovered significant associations between the constructs, highlighting the critical role of perceived knowledge in shaping perceptions of ease of use (0.726), the influence of trust in chatbot systems on feelings of empowerment in managing health (0.673), and the effect of empowerment on patient attitudes toward adopting AI chatbots (0.792). The model explained 79.2% of the variance in Attitude and 59.3% in Behavioral Intention to use AI chatbots as a tool for health treatment.
CONCLUSIONS: The findings provide actionable insights for healthcare providers and policymakers aiming to promote AI chatbot adoption in medical contexts. Key factors influencing adoption include the dissemination of chatbot technologies within the general population, the availability of alternative healthcare options, and patients' perceptions of their knowledge and confidence in using such tools. Moreover, fostering trust in chatbot systems and emphasizing their role in empowering patients to manage their health is essential for encouraging sustained use. These results contribute to the growing literature on digital health adoption and offer strategic guidance for effectively integrating AI chatbots into healthcare systems.