# Utilization of Machine Learning Algorithms
## 3 Results
### Machine Learning Interventions for Enhanced HIV Testing
#### 3.2.2 Machine Learning Interventions Employed to Enhance HIV Testing
Similar studies conducted in the USA developed models that effectively reduced HIVfalse-positive results [22; 57]. One of the South African studies we reviewed developed AI conversational agents for automated HIV self-counseling and testing [17]. The AI conversational agents/models were developed on Android phones, enabling participants to conduct self-HIV counseling, risk determination, rapid testing, and linkage to care for positive cases. Out of the ten participants in the study, six testers found that talking to the agent felt natural and equivalent to chatting with a human. At the same time, seven said they would feel comfortable taking a real HIV test with the agent. The study argues that this method is more effective than traditional HIV testing and counseling due to its anonymity, privacy, speed, and easy access [17]. The findings of this study underscore a significant demand for the application of mobile-based algorithms for efficient, accurate, automated, and cost-effective HIV testing. An inbuilt mHealth model, combined with DL, demonstrated the ability to reduce false-negative HIV RDT test results with high levels of sensitivity and specificity compared to traditional HIV rapid diagnostic tests in South Africa [48]. HealthPulse, another AI-based mobile algorithm designed to detect and interpret RDTs for HIV in low- and middle-income countries (LMICs), not only enhances RDT efficiency but also prioritizes privacy, accountability, and accessibility [50; 55]. Moreover, ML interventions have demonstrated high accuracy in HIV prediction and detection in Ethiopia, Kenya, and Uganda [33; 49; 55] (Table 3; also see Table S6). * Enhancing the uptake of HIV self-testing among MSM using machine learning: The WHO recommends HIVST as a convenient and confidential option for HIV testing, especially among KPs who are often reluctant to access facilities for counseling and testing services [7]. This review synthesized studies that used ML to improve the uptake and utilization of HIVST among MSM from different countries. In China, an ML approach increased the efficiency of HIVST by 17.7%, and the distribution of HIVST kits was improved by 18% [39]. In a mixed study from Malaysia, an AI Chatbot was compared with human HIVST counseling and testing [60]. The study revealed that 93% of the participants perceived that the AI Chatbot provided more comprehensive information about testing, 100% of them stated that the chatbot was more convenient, and 79% were willing to continue using it [60]. [PERSON] et al. [63] used a Greedy algorithm to increase the economic benefits of the secondary distribution of HIVST kits by more than 23% compared to those achieved by conventional methods. However, the uptake of HIVST is still low in many developing countries as the services are more friendly to individuals who can read and comprehend English. One of the recommendations from [PERSON] et al. [60] is to develop ML-aided HIVST technologies suitable for illiterate people. This study agrees with these recommendations since the novel technique is required more in developing countries like SSA, with its high HIV prevalence and high rates of illiteracy among its population.