Zhao Ni, Sunyoung Oh, Rumana Saifi, Frederick Altice, Iskandar Azwa
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引用次数: 0
Abstract
Background: Malaysia, an upper middle-income country in the Asia-Pacific region, has an HIV epidemic that has transitioned from needle sharing to sexual transmission, mainly in MSM. Men who have sex with men (MSM) are the most vulnerable population for HIV in Malaysia. In 2022, our team developed a web-based artificial intelligence (AI) chatbot and tested its feasibility and acceptability among MSM in Malaysia to promote HIV testing. To enhance the usability of the AI chatbot, we made it accessible to the public through the website called MYHIV365 and tested it in an observational study.
Objective: This study aimed to test the usability of an AI chatbot in promoting HIV testing among MSM living in Malaysia.
Methods: An observational study was conducted from August 2023 to March 2024 among 334 MSM. Participants were recruited through community outreach and social-networking apps using flyers. The interactions between participants and the AI chatbot were documented and retrieved from the chatbot developer's platform. Data were analyzed following a predefined metrics using R software (Posit Software, PBC, Boston, USA).
Results: The AI chatbot interacted with 334 participants, assisting them in receiving free HIV self-testing kits, offering information on HIV, PrEP, and mental health, and providing details of 220 MSM-friendly clinics, including their addresses, phone numbers, and operating hours. After the study, 393 human-chatbot interactions were documented on the chatbot developer's platform. Most participants (304/334, 91%) interacted with the AI chatbot once, 30 (9%) engaged 2 or more times at different intervals. Participants' interaction time with the chatbot varied, ranging from 1 to 31 minutes. The AI chatbot properly addressed most participants' questions (362/393, 92.1%) about HIV and PrEP. However, in 31 interactions, participants posed additional questions to the chatbot that were not programmed into the chatbot algorithm, resulting in unanswered interactions.
Conclusions: The web-based AI chatbot demonstrated high usability in delivering HIV self-testing kits and providing clinical information on HIV testing, PrEP, and mental health services. To enhance its usability in community and clinical settings, the chatbot must offer personalized health information and precise interaction, powered by a sophisticated machine learning algorithm. Additionally, establishing an effective connection between the AI chatbot and healthcare systems to eliminate stigma and discrimination towards MSM is crucial for the future implementation of AI chatbots.
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