Medical Consultation and HIV Testing After AI-Based Symptom Check: Retrospective Cohort Study

Promoting early HIV testing and patient detection is an important public health goal. In Japan, approximately 30% of the population is diagnosed with AIDS. Several studies have investigated the challenges related to HIV diagnosis; however, there are limitations in understanding the characteristics and barriers faced by individuals who are at high risk of HIV but have not yet been tested or have not sought medical consultation.

This study aimed to examine the factors associated with medical consultation and HIV-testing behaviors, explore the reasons for not undergoing HIV testing, and evaluate the effectiveness of HIV-related awareness efforts among respondents to a revisit survey conducted via an artificial intelligence (AI)-based symptom search engine.

This retrospective cohort study used data obtained from the AI-based symptom search engine, Ubie. Episodes involving individuals who used the AI-based symptom checker to search for their symptoms, which were subsequently suggested as HIV/AIDS/sexually transmitted infection (STI)-related conditions, were included. Those who answered the first and revisit survey questionnaires were included in the analysis. Multivariable logistic regression analyses were conducted to explore the factors associated with medical consultation in both the overall suggested HIV/AIDS/STI-related condition group and the suggested STI-related condition subgroup. Factors associated with HIV testing in individuals who underwent medical consultations were also explored using multivariable logistic regression analysis. The reasons for not undergoing HIV testing and the future intention to undergo testing were described.

The number of eligible episodes was 424,893 for 332,976 individuals. Of these, medical consultations were performed in 105,365 cases and HIV testing in 394 cases. Compared with individuals in their 20s, older age groups were associated with a higher tendency to seek medical consultations. The provision of awareness information through the AI-based symptom checker was associated with medical consultation behavior, and 29% (280/964) of people who initially had no intention of undergoing HIV testing responded that they would undergo HIV testing after using the AI-based symptom checker. Compared with the internal medicine department, the gynecology department was significantly associated with HIV testing; however, the HIV testing rates were low in the suggested STI-related condition subgroup across major departments.

These results suggest that HIV-related information delivered via an AI-based symptom checker may raise awareness or consideration of medical consultation among individuals actively searching for symptoms potentially associated with HIV. To further promote HIV testing, it may be necessary to refine the content and delivery of educational materials and enhance HIV testing literacy among physicians who encounter patients with STIs.