## 3 Results  
### Study Characteristics  
Table 2 outlines the characteristics of the 20 studies included in this review on the application of AI technologies in STI diagnosis and management across SSA [37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]. Only 20 of 49 SSA countries [57] reported on this subject, with Southern Africa being the most studied region, led by South Africa and Zimbabwe, followed by Malawi, Eswatini, Namibia, Lesotho, Mozambique, and Angola, totaling 11 studies. East Africa contributed 10 studies, with Ethiopia leading followed by Kenya, Tanzania, Rwanda, Uganda, and Burundi. In West Africa, Cote d'Ivoire had only two studies followed by Guinea, Liberia, Nigeria, and Sierra Leone together contributing to five studies, while Central Africa was represented by only one study from Cameroon. Notably, one study included countries across all four subregions [44], and two studies spanned East and Southern Africa [54, 51]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author, Year, Region** & **Study Design** & **Population and Sample Size** & **All Approaches** & **Implementation Classification and Comparator** & **Main Findings** & **Outcomes** \\ \hline \multirow{4}{*}{[PERSON] et al., 2023, West Africa [37]} & \multirow{4}{*}{Retrospective observational study.} & Students, staff, and non-staffle (males and females, aged 9-60 years, n = 400). & Logistic regression, KNN, decision tree, naive Bayes, random forest, and AdaBoost. & Methodological, no comparator & Logistic regression & \multirow{4}{*}{Classification accuracy, AUC, model for diagnosing STIs, the logistic activeancy.95\%} & Classification accuracy, AUC, recall, and F-score of the logistic activeancy.95\% & Classification accuracy, AUC, regression model. \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] 2024, East Africa [39]} & \multirow{4}{*}{Retrospective study.} & \multirow{4}{*}{Advescent respondents (n = 45(2)).} & J48 decision tree, random forest, SVM, multi-layer perceptron, naive Bayes, logistic gradient boosting, and logistic regression. & \multirow{4}{*}{Methodological, no comparator (n = 45(2)).} & J48 decision tree & \multirow{4}{*}{Classification accuracy, AUC, model for diagnosing STIs, the logistic activeancy.95\%} & \multirow{4}{*}{regression model.} \\  & & & & J48 decision tree, random forest, SVM, multi-layer perceptron, naive Bayes, logistic gradient boosting, and logistic regression. & \multirow{4}{*}{Methodological, no comparator (n = 45(2)).} & \multirow{4}{*}{Identifological, no predictor testing (HIV)} & \multirow{4}{*}{Knowledge of HIV and predictors based on awareness of on awareness of on aVIDS and STIs.} \\ \hline \multirow{4}{*}{[PERSON] et al., 2023, East Africa [40]} & \multirow{4}{*}{Observational study.} & Both genders aged \(\geq\)18 years (n = 854). & Al algorithms using computer vision technology. & \multirow{4}{*}{All algorithms using self-interpretation, pharmacy provider interpretation, and expert panel interpretation.} & \multirow{4}{*}{All vision technology reduced false negatives, identified predictive value, negative predictive value, and detection of missed infections.} \\ \hline \multirow{4}{*}{[PERSON] et al., 2020, East Africa [38]} & \multirow{4}{*}{Retrospective observational study.} & \multirow{4}{*}{Males and females aged \(\geq\)15 years (n = 75,558).} & Logistic regression, penalized logistic regression, generalized additive models, stepwise logistic regression, and ML algorithms. & \multirow{4}{*}{Methodological, risk group, and model-based approaches} & \multirow{4}{*}{Methodological, risk identifying and individuals at risk} & \multirow{4}{*}{Afficiency and identifying} \\  & & & & & & \\ \cline{1-1} \cline{4-4}