## 3 Results
### Reported Challenges, Evaluations, and Future Directions for AI Technologies in Healthcare
#### 3.6.2 Evaluation of Artificial Intelligence Approaches by the Reviewed Studies  
The reviewed studies highlighted the widespread use of AI and ML approaches for data analysis. These methods consistently outperformed traditional, non-AI approaches, demonstrating high levels of precision, accuracy, and sensitivity. However, further validation of these outcomes with diverse datasets and settings is essential to confirm their superiority and relevance. Future research should also focus on identifying other factors contributing to the success of these models and determining the optimal conditions under which one model may outperform another. Additionally, it is critical to calculate comprehensive performance metrics and optimize models for more effective predictions. A deeper understanding of how AI approaches can be integrated into primary healthcare, especially in SSA, is vital. This includes considering the settings, data sources, and the quality of tech  
\begin{table}
\begin{tabular}{l l} \hline \hline
**AI Approach** & **Use of AI Approaches and Metrics** \\ \hline \multirow{7}{*}{1. Random forest} & (a) Predicting virological failure—sensitivity: 1.00, precision: 0.987, F1-score: 0.993, AUC: 0.9989 \\  & (b) Predicting clinic visits and viral load suppression: \\  & (i) Clinic visits—accuracy: 66–79%, sensitivity: 60.6\%, specificity: 67\%, negative predictive value: 94\%. \\  & (ii) Viral load suppression—accuracy: 76%, sensitivity: 65.6\%, negative predictive value: 95\%. \\  & (c) Accuracy of ML algorithms—accuracy: 85\%. \\  & (d) Predicting and visualizing HIV index testing—no specific metrics provided. \\ \hline \multirow{7}{*}{2. XGBoost} & (a) Predicting likelihood of HIV infection—F1-score: 91.4\% for males, 90.1\% for females. \\  & (b) Enhancing HIV positivity identification—F1-score: 90\% for males, 92\% for females. \\  & (c) Predicting HIV status—mean F1-score: 76.8\% for males, 78.8\% for females. \\  & (d) Accuracy of ML algorithms—accuracy: 83.89\%. \\  & (e) Predicting and visualizing HIV index testing—no metrics provided. \\ \hline \multirow{7}{*}{3. Logistic regression} & Diagnosis and prediction of STD infection accuracy—classification accuracy: 95\%, AUC: 94.6\%, recall: 93.9\%, \\  & F-score: 91.1\%. \\  & Predicting HIV status—accuracy of 85\%, recall of 98\%, and F1-score of 92\%. \\ \hline
4. ML models & HIV acquisition risk identification, predicting interruptions in treatment—no metrics specified. \\ \hline
5. AI agent & HIV counseling and testing—user acceptance: more than 60\% found it natural, 70\% felt comfortable, 60\% guided to complete the test, \\ \hline
6. AI algorithm & HIV testing—sensitivity: 100\%, negative predictive value: 100\%, specificity: 99\%, positive predictive value: 81.5\%. \\ \hline \multirow{3}{*}{7. DL models} & HIV status prediction: \\  & (a) RNN accuracy: 87\%, precision: 87\%, recall: 87\%, F1-score: 87\%, AUC: 94\%. \\  & (b) ANN accuracy: 85.5\%, precision: 84.4\%, recall: 85.7\%, F1-score: 85.1\%, AUC: 89.72\%. \\ \hline
8. J48 decision tree & HIV detection: accuracy: 81.29\%, ROC curve: 86.3\%. \\ \hline
9. LASSO model & Identifying HIV testing uptake factors—no metrics specified. \\ \hline
10. Unsupervised ML approaches & Identifying male and female HIV clusters—no metrics specified. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best performing artificial intelligence approaches.  
nical resources available. Validation of these approaches in real-world primary healthcare settings remains a key area for further investigation.