Background
Self-harm and suicidal ideation are significant health concerns among people with or vulnerable to HIV (PWH), who frequently experience stigma and related mental health challenges such as stress, anxiety, and depression. The AI-powered MARVIN chatbot provides HIV self-management-related knowledge and assists with medication adherence (antiretroviral therapy and pre-exposure prophylaxis). It also provides a safe space for PWH to ask sensitive questions which is amenable to mental health support. To enable MARVIN to manage high-risk messaging linked to stigma and mental health issues, we developed a preventive intervention module to address extreme user intentions.
Methods
Following the CO-STAR framework, ChatGPT was prompt-tuned to identify 3 types of message intent: self-harm, insult, and non-extreme (i.e., any other intent). To test its performance, we compiled three public hate speech databases from an online catalog (hatespeechdata.com) and combined them with MARVIN-user conversations and a synthetic dataset (N=1000 for each class). We computed precision, recall, and F1 Score for each class, as well as overall accuracy. After integrating ChatGPT into MARVIN, three PWH, two engineers, and a doctor, participated in a two-hour test by performing 14 conversational scenarios and completing a two-item questionnaire on conversation clarity and user satisfaction.
Results
With one-shot prompting, ChatGPT attained 97.00% and 94.90% for recall on self-harm and insult intent, respectively. The overall accuracy reached 95.57%, with the remaining metrics shown in Figure 1. The ChatGPT-MARVIN hybrid model then successfully generated appropriate responses containing 1) emergency contact information for self-harm intents; 2) messages guiding users to use stigma-free expressions for insult messages; and 3) a response reviewed by a medical expert for a non-extreme intent. All six testers found MARVIN’s responses to be clear and concise and were satisfied with the overall experience. However, one patient participant suggested including links to additional resources.
Conclusions
Testing this anti-stigma preventive module integrated into MARVIN underscored its strong capacity to correctly identify extreme intents and generate concise, satisfying responses. Our findings suggest that AI-driven chatbots, viewed as safe spaces, could play a pivotal role in promoting self-management strategies while mitigating stigma and providing users with non-judgmental mental health support.