## 3 Results
### Study Characteristics
Two studies identified HIV predictors for screening [51] and clusters via unsupervised ML where the model is trained on unlabelled data without human supervision [44]. Image-based studies used AI algorithms including DL for interpreting HIV self-testing results (39) and classifying rapid HIV test images [52], and one study examined HIV counseling effectiveness using a conversational agent [56]. Participant demographics varied widely, including students, staff, and non-staff aged 9-60 years [38]; adolescents with unspecified ages [37]; males aged 15-54 and females aged 15-49 [41]; ART patients with a median age of 33 years (IQR 27-41) [45]; and HIV-positive adults aged \(\geq\)18 years [42]. Some studies included only women aged 15-49 [55] or unspecified image library samples [52]. Other studies sampled populations aged 15-49 [39], 15 years and above [53], and \(\geq\)18 years [47]. Reported ages included a median of 39 years (IQR 31-49) [50], an average age of 31.8 years for males and 32.1 for females [44], and 30.2 years (s.d. 2.3) for combined genders [56]. Some studies specified only the participant gender [46; 51; 54], while others focused on specific demographics, such as MSM without age details [48], or used narrow age bands [43]. Study sample sizes ranged broadly, from n = 10 to n = 809,977. The study populations were derived from different data sources with most studies (10) using data from population-based assessments, demographic and health surveys (DHS), and community surveys; followed by data from health facility clinical records (4); HIV programmatic data and bio-behavioral data including rapid HIV test images (3), specifically population data (2) and data from purchasing sexual health products from pharmacies (1) as shown in Figure 3.  
Figure 3: Study populations of reviewed studies. The distribution of the reviewed studies, according to the subregions, countries, and years of publication, was analyzed. Eight countries from Southern Africa contributed to 11 studies published between 2017 and 2023 out of the reviewed 20, followed by six countries in East Africa contributing to 10 studies published between 2020 and 2024, five countries in West Africa contributing to three studies between 2022 and 2023, and Central Africa contributed to one study in 2023 (Figure 4). The Artificial Intelligence Approaches Used in the Diagnosis and Management of Sexually Transmitted Infections  
Several AI approaches are used in the diagnosis and management of STIs in SSA. This study found that SSA primarily uses AI methods, including ML, for secondary data analysis. The focus is on extracting actionable insights from existing health records, surveys, and surveillance data to tackle significant healthcare challenges. Table 2 describes the AI and ML approaches used to diagnose and manage STIs using supervised ML, unsupervised ML, and DL algorithms [37; 38; 41; 45; 47; 49; 50; 53; 54; 55]. Logistic regression and its advanced variations, including elastic net (which addresses multicollinearity) and stepwise logistic regression (used for feature selection), were effectively employed to diagnose STIs, predict HIV status, and assess infection risks. These methods enabled more accurate, data-driven public health interventions in SSA. These methods also helped predict treatment interruptions, clinic attendance, and viral load suppression [37; 38; 41; 45; 50; 53; 55; 57]. In addition, the NNM included advanced architectures, like MLP, CNNs, RNNs, LSTM networks, and CNNLSTM hybrids. Multilayer perceptron (MLP), CNN, ResNet50, and LSTM were used for predicting HIV test status, developing prognostic tools for HIV /AIDS, and building predictive models for HIV index testing and status [40; 43; 46; 48]. Furthermore, the tree models, including decision trees and XGBoost, addressed STI diagnosis, HIV testing, and virological failure prediction, and developed predictive models for HIV status and index testing [37; 38; 41; 42; 43; 46; 54; 55].