## 4 Discussion  
The systematic review and meta-analysis sought to explore the application of AI technologies in improving STI diagnosis and management, as well as to describe associated successes and gaps in SSA. Among the 49 countries in SSA, only 20 (41%) have adopted AI approaches for diagnosing and managing STIs. Southern Africa demonstrated the highest adoption rate at 80%, followed by East Africa at 40%, West Africa at 33%, and Central Africa at 11%. These findings align with other studies [58, 59], highlighting the unequal availability and access to technology globally. Developing countries, particularly in SSA, lag in adopting AI technologies, while developed nations benefit from an abundance of these advancements [59, 60]. The current study advocates for the adoption of AI approaches in SSA, given that the region bears the highest burden of STIs, particularly HIV [61, 62, 63, 64]. Southern and East Africa's higher representation in AI applications reflects ongoing efforts in regions hardest hit by STIs and HIV [65, 66]. This aligns with findings [37, 67, 68, 69, 70, 71] that countries like Ethiopia, Kenya, South Africa, Uganda, and Zimbabwe have integrated AI approaches into data analysis to address high STI and HIV prevalence. Similarly, West African countries, such as Cote d'Ivoire, Nigeria, and Sierra Leone, are also making strides in adopting AI technologies due to their significant STI burden [49, 68, 72]. Central Africa, however, remains far behind, with Cameroon being the only country represented in the reviewed studies, consistent with findings from [73]. The data originate from diverse sources, including population-based assessments (e.g., DHS), community surveys, health facility records, bio-behavioral HIV data, rapid HIV test images, specific population studies, and purchasing data for sexual health products. This diversity enriches AI applications by providing multi-dimensional insights but poses challenges in harmonization and integration due to differences in type, scale, and focus [74]. While large-scale datasets, like DHS, offer generalizable insights, smaller datasets provide depth and specificity. Heterogeneity requires robust preprocessing and \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Test** & **Statistic** & \(p\)**-Value** & **Conclusion** \\ \hline Kruskal–Wallis test for F1-scores & H = 6.5895 & 0.0862 & Fail to reject the null hypothesis \\ \hline Kruskal–Wallis test for sample sizes & H = 34.6 & 0.000 & Reject the null hypothesis \\ \hline ANOVA for F1-scores & - & 0.0228 & Reject the null hypothesis \\ \hline ANOVA for sample sizes & F = 23.68 & 0.000 & Reject the null hypothesis \\ \hline [PERSON]’s test for F1-scores & \multicolumn{2}{c}{0.0314} & Reject the null hypothesis \\ \cline{3-4}  & \multicolumn{2}{c}{(Southern Africa vs. East Southern Africa vs. West Africa; East Southern Africa vs. West Africa; East Southern Africa vs. West Africa; East Southern Africa vs. West Africa vs. Southern Africa)} & Reject the null hypothesis \\ \hline [PERSON]’s test for F1-sample sizes & \multicolumn{2}{c}{East Africa vs. Southern Africa} & Fail to reject the null hypothesis \\ \hline Correlation between F1-scores and sample sizes & r = 0.0055 & 0.9643 & Fail to reject the null hypothesis \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Subgroup analyses tests and results. standardization to ensure accuracy and reliability, and biases must be managed to avoid skewed AI outcomes [75]. Algorithmic bias in healthcare happens because datasets are not created equally. The risk of algorithmic bias in healthcare stems from systemic inequalities in dataset curation, disparities in opportunities for participation in research, and inequitable access to healthcare services [76]. These challenges can result in datasets that inadequately represent diverse populations, leading to biased AI models and suboptimal outcomes for underrepresented groups [77]. By addressing these challenges, rich and varied data can effectively support robust and unbiased AI-driven solutions.