The proliferation of AI-driven healthcare chatbots has catalyzed a paradigm shift in the healthcare industry, necessitating a deeper understanding of how their communication styles influence potential health outcomes. Drawing upon the Computers are Social Actors (CASA) framework, this study employs experimental design to interrogate the differential effects of dominant and affiliative communication styles on patients’ adherence to health information. The findings reveal that an affiliative communication style, characterized by empathy, warmth, and patient-centered language, significantly augments treatment adherence by fostering patients’ perceived involvement in shared decision-making and engendering trust in the chatbot. Notably, the study illuminates the absence of a significant moderating effect of patients’ self-construal on the relationship between chatbot communication style and treatment adherence, suggesting the potential for a universally efficacious communication strategy. Moreover, the study corroborates the parallel mediation effects of shared decision-making and patient trust, underscoring their pivotal role in the causal pathway between chatbot communication style and treatment adherence. The study propounds the imperative for adopting an affiliative communication style as a universally applicable strategy to optimize patient engagement and treatment adherence, transcending the boundaries of individual differences. Methodologically, this study pioneers a novel approach to manipulating chatbot communication styles through meticulously crafted prompts, presenting a valuable methodological innovation for future research endeavors. The theoretical and practical implications of this study are far-reaching, informing the development of patient-centered, trust-enhancing, and adherence-promoting AI technologies in healthcare.