## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Author, Year** & **Region** & **Study Design** & **Population and Sample Size** & **AI Approaches** & **Implementation Classification and Comparator** & **Main Findings** & **Outcomes** \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al., 2020, SSA \\ Regions [54] \\ \end{tabular} } & \multirow{3}{*}{Retrospective study.} & \multirow{3}{*}{Males and females} & Penalized logistic regression, generalized additive model, SVM, and XGBBoost. & \multirow{3}{*}{Methodological, no} & XGBoost algorithm & Classification of HIV \\  & & & & & \begin{tabular}{c} accuracy in \\ specified \\ \end{tabular} & & \begin{tabular}{c} rapid diagnostic test \\ images. \\ \end{tabular} \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al., 2021, \\ Southern Africa [55] \\ \end{tabular} } & \multirow{3}{*}{Objective research design.} & \multirow{3}{*}{Image library} & Deep learning algorithms: & \multirow{3}{*}{Methodological, traditional visual} & DL algorithms & \multirow{3}{*}{\begin{tabular}{c} DH, algorithms \\ achieved 98.9\% \\ \end{tabular} } \\  & & & ResNet50, & & & \begin{tabular}{c} accuracy in \\ outperforming \\ traditional visual \\ \end{tabular} & & \multirow{3}{*}{\begin{tabular}{c} High accuracy in \\ ROTI image \\ classification. \\ \end{tabular} } \\  & & & MobileNetV3, & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al., \\ 2017, Southern Africa [56] \\ \end{tabular} } & \multirow{3}{*}{Infodemiology approach.} & Males and females & HIV counseling and testing using & \multirow{3}{*}{\begin{tabular}{c} Methodological, \\ testing using \\ consressional \\ agents. \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Methodological, \\ human counseling, \\ and testing. \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} Agents were natural \\ and equivalent to \\ human counseling, \\ fostering user \\ openness. \\ \end{tabular} } \\  & & & & & \\ \hline \multirow{6}{*}{
\begin{tabular}{c} [PERSON] et al., \\ 2017, Southern Africa [56] \\ \end{tabular} } & \multirow{3}{*}{Infodemiology approach.} & HIV, human immunodeficiency virus; STI, sexually transmitted infections; RNN, recurrent neural networks, AI, artificial intelligence; ML, machine learning; DL, deep learning, IQR, interquartile range; MSM, men who have sex with men; s.d., standard deviation; HIVST, HIV self-testing; VCT, voluntary counseling and testing; AIDS, acquired immunodeficiency syndrome; SSA, Sub-Saharan Africa; CNNRNN, convolutional neural network–recurrent neural network; CNNLSTM, convolutional neural network–long short-term memory network; AUC, area under the curve; Api.AI, artificial intelligence application programming interface. N.B. The comparator refers to the reference or baseline method against which the AI technologies were evaluated in the studies. \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: Studies on the diagnosis and management of STIs published in SSA from January 2017 to May 2024. (n = 1) that is focused on analyzing health-related information available in digital spaces like online searches, news media, blogs, and social media, institution-based cross-sectional studies (n = 1), and institution-based longitudinal studies (n = 1), where secondary data were used. Most studies (n = 11) focused on STI prediction, with 10 specifically predicting HIV status. Only one study examined STIs broadly without focusing on HIV/AIDS [38]. Nineteen studies primarily targeted HIV, with one uniquely applying RNN and ML techniques to predict HIV status among MSM [48]. Other predictive studies addressed HIV prevalence [49], virological failure [42], treatment interruptions [45], and clinic attendance and viral load suppression [50]. Additional studies (n = 3) aimed to develop prognostic tools for HIV/AIDS using DL models [40], ML models for HIV index testing [43; 46], and ML risk scores for HIV acquisition risk [53].