## 3 Results
### Meta-Analysis
#### 3.7.1 Descriptive Analysis  
Uses of Artificial Intelligence and Machine Learning Approaches  
The meta-analysis findings corroborate the earlier qualitative analysis, confirming that AI/ML approaches were predominantly used for predicting HIV status, HIV infection, virological failure, and HIV outcomes. Additionally, these approaches were employed to identify HIV predictors for screening, develop HIV/AIDS prognostic tools, and diagnose and predict sexually transmitted diseases (STDs). Sample Sizes and F1-Scores  
In the meta-analysis, Table 5 shows that the reviewed studies had a mean sample size of 29,471.67 and a median of 6672, highlighting a significant disparity between the two measures. This suggests that, while some studies had very small sample sizes, others were exceptionally large, reflecting that the distribution of sample sizes across the studies is likely right-skewed, indicating that there are many small studies and only a few very large ones. This skewness may be primarily due to the presence of a few studies with significantly large sample sizes. The sample sizes ranged from a minimum of 400 to a maximum of 87,044, yielding a range of 86,644, which underscores the substantial variability across studies. This variability is further reflected by a standard deviation of 36,363.5, indicating that the sample sizes are widely dispersed within the given range. These findings are confirmed by the box and whisker plot in Figure 5, showing a very wide rectangle in the upper 50% of the sample size data and a narrow rectangle representing the lower 50%. \begin{table}
\begin{tabular}{l c c} \hline
**Metric** & **Frequency** & **Rank** \\ \hline F1-Score & 9 & 1 \\ \hline Accuracy & 9 & 1 \\ \hline Sensitivity & 8 & 3 \\ \hline AUC (Area Under the Curve) & 7 & 4 \\ \hline Recall & 6 & 5 \\ \hline Specificity & 5 & 6 \\ \hline PPV (Positive Predictive Value) & 4 & 7 \\ \hline NPV (Negative Predictive Value) & 4 & 7 \\ \hline Precision & 4 & 7 \\ \hline Log-Loss & 3 & 10 \\ \hline Misclassification Rate & 1 & 11 \\ \hline Feature Importance & 1 & 11 \\ \hline \end{tabular}
\end{table} 
Table 4: Metric measures used in the reviewed studies. \begin{table}
\begin{tabular}{l c c} \hline
**Statistic** & **Sample Size** & **F1-Score** \\ \hline Mean & 29,471.67 & 0.7886 \\ \hline Median & 6672 & 0.811 \\ \hline Minimum & 400 & 0.114 \\ \hline Maximum & 87,044 & 0.993 \\ \hline Standard Deviation & 36,363.5 & 0.163 \\ \hline \end{tabular}
\end{table} 
Table 5: Descriptive statistics. In addition, the mean F1-score was 0.7886, and a median of 0.811 showed a small difference between the two metrics. However, the minimum F1-score was 0.114, highlighting the poor performance of AI/ML approaches in some studies, and the maximum was 0.993, reflecting the good performance of AI/ML approaches in some of the reviewed studies, giving a range of 0.879. A standard deviation of 0.163 may reflect that there are mild differences among the F1-scores. These findings are summarized in the box and whisker plot in Figure 6, showing almost symmetrical data with the lower and upper 50% of the F1-scores, occupying almost equal spaces in the box. The box plot shows that there are about five outliers in the F1-scores, shown by the dots below the lower whisker. The quantitative variables, sample size, and F1-score were assessed for any relationships or associations. The scatter plot in Figure 7 does not show a clear pattern of the relationship between F1-scores and sample size. It shows that AI/ML approaches performed well with small samples, and they also performed poorly with small samples to a lesser extent. Similarly, most of the AI/ML approaches performed well with large samples except for two studies that had F1-scores below 0.5. The absence of a clear trend or pattern may imply that AI/ML approaches' performance may be influenced by other factors, not necessarily the sample size. Figure 5: Box and whisker plot for the sample sizes. Figure 6: Box and whisker plot for the F1-scores.