A Health Informatics Framework for Integrating Machine Learning and Generative AI in HIV Risk Stratification and Personalized PrEP Recommendation

Panyaphon Phiphatkunarnon, Amornphat Kitro, Benjamas Suksatit, Boon-Leong Neo, Do Tran, Worawit Tepsan

Background: Although pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, identifying individuals who may benefit from PrEP and delivering personalized prevention recommendations remain challenging in routine and digital health settings. Objective: This study aimed to develop and preliminarily evaluate an integrated artificial intelligence framework combining machine learning (ML) for HIV risk stratification and generative artificial intelligence (GenAI) for personalized PrEP recommendation support. Methods: A curated dataset of 2000 de-identified client profiles from Love2Test platform was used for proof-of-concept model development. Profiles were labeled as low or high HIV acquisition risk by domain experts based on structured behavioral information. Multiple ML classifiers were trained and compared using PyCaret. The selected model was integrated with a generative AI model through structured prompting to generate personalized PrEP recommendation content. The integrated framework was evaluated through structured physician assessment by four independent medical doctors. Results: The selected model showed strong internal discrimination for classifying high versus low HIV acquisition risk. The integrated framework also received favorable physician evaluation for clinical accuracy, explanation validity, contextual relevance, and error minimization across fixed and randomly selected profiles. However, because expert labeling was based on structured behavioral indicators closely related to the model inputs, the high internal performance should be interpreted within the context of this proof-of-concept study. Conclusions: The proposed framework provides a structured approach to support HIV risk stratification and personalized PrEP recommendations in a clinician-aligned manner. However, this study was an offline proof-of-concept and did not directly evaluate patient interaction, PrEP uptake, stigma, adherence, or clinical outcomes. Prospective studies using larger and more representative real-world datasets are needed to assess implementation, generalizability, and impact on service engagement and PrEP initiation.