## 4 Discussion
The study highlights the effectiveness of various AI and ML methods in managing HIV and STIs through detection, diagnosis, treatment, and monitoring. Algorithms such as random forest, AdaBoost, GradBoost, XGBoost, K-nearest neighbors, and support vector machines have proven valuable for disease prediction, particularly in identifying high-risk individuals to target prevention efforts [78; 79]. Decision trees, multilayer perceptions, and logistic regression models are also effective for disease prediction and management [80]. Moreover, advanced methods, such as decision trees (J48), LASSO models, and DL approaches, aid in diagnosing and predicting STIs, addressing infection risks, tracking hotspots, and analyzing community spread [81; 82; 83]. Applications in image analysis, STI testing, and counseling have significantly improved diagnostic and treatment access, consistent with broader applications in medicine [84; 85; 86; 87; 88; 89]. Artificial intelligence's role in point-of-care diagnosis, test interpretation, and outbreak management supports hospital activities and clinical decision-making [90; 91]. Despite these advancements, SSA faces challenges in adopting WHO-recommended, molecular-based STI testing due to a lack of accessible, accurate, and affordable diagnostic tools [7]. Syndromic management remains widely used yet fails to address approximately 70% of asymptomatic curable STIs, increasing untreated risks [87; 88; 92]. Integrating ML, big data analytics, and statistical approaches could enable personalized, predictive, and preventative healthcare through electronic health records for STIs [89]. These advancements could help SSA achieve the UNAIDS 95-95-95 targets to end the HIV epidemic by 2030. However, implementing AI technologies in healthcare faces several challenges. The absence of technical guidelines, regulatory frameworks, and skilled personnel creates uncertainty [85; 87]. These challenges are compounded by unreliable health data, model specification issues, and insufficient clinician expertise [93; 94; 95; 96]. Privacy, security concerns, and the involvement of diverse stakeholders further complicated implementation. In SSA, limited access to large datasets necessary for AI predictions using supervised modeling is hindered by inadequate data collection infrastructure and a lack of standardization in data formats [93; 94]. While countries like South Africa have embraced AI technologies, many SSA nations face barriers, such as poverty, unemployment, and inadequate policy frameworks [97; 98; 99]. Limited collaboration between public and private sectors and a lack of data repositories further hinder progress [100]. Advanced techniques, like NLP, CNNs, and AI-driven drug discovery, remain underutilized in SSA. In contrast, regions like North America and Europe employ these methods to enhance the diagnosis and management of diseases, including STIs [101; 102; 103; 104; 105]. Bridging this technological gap through international collaboration and capacity building is critical for SSA to leverage advanced AI/ML technologies effectively. Despite these challenges, AI/ML holds promise for improving care, reducing costs, and supporting the management of infectious diseases, like HIV and STIs [96; 106]. By moving beyond syndromic management, SSA can embrace ML and big data analytics to make informed decisions, achieve UNAIDS targets, and advance precision medicine [107; 108; 109; 110]. African governments and international partners must invest in robust STI diagnosis and management systems to realize the full potential of 4 IR technologies [62; 63]. The finding that all reviewed studies were methodological underscores the early-stage development of AI methods in STI diagnosis and management within SSA [111]. This reflects a significant focus on model development and evaluation, highlighting AI's potential to address public health challenges. However, the absence of real-world testing emphasizes the need for further research to understand how these methods perform in diverse clinical and community settings.