Leveraging artificial intelligence and nursing care to facilitate HIV PrEP utilization among Black cisgender women: a qualitative study

HIV pre-exposure prophylaxis (PrEP) is a proven strategy for preventing HIV transmission, yet Black cisgender women remain underrepresented despite their elevated risk. Barriers such as stigma, mistrust, logistical challenges, and limited awareness hinder PrEP uptake in this population. Artificial intelligence (AI)-powered chatbot offers promising solutions by providing personalized, non-judgmental, and accessible education. This study aimed to identify barriers and facilitators to PrEP care among Black cisgender women and to explore stakeholder preferences for chatbot features. We conducted a qualitative study using semi-structured interviews with nine PrEP-eligible Black cisgender women and eight nurses providing HIV prevention services. Data were analyzed using grounded theory to identify themes related to PrEP barriers, chatbot features, and stakeholder preferences. Participants identified stigma, mistrust, financial challenges, and educational gaps as primary barriers to PrEP uptake. Chatbots were viewed as valuable tools for addressing these barriers by fostering privacy, providing tailored education, and enhancing accessibility. Black women emphasized the importance of empathy, conversational tones, and cultural sensitivity in chatbot design, while nurses highlighted the need for accuracy and robust privacy measures. Nurses were also seen as critical facilitators, bridging gaps in chatbot functionality and ensuring trust and cultural alignment. This study provides foundational insights for designing culturally sensitive, patient-centered chatbots.