## 1 Introduction  
The Sub-Saharan Africa (SSA) region carries a disproportionately high burden of sexually transmitted infections (STIs), with a significant prevalence and incidence within individual countries. According to the World Health Organization (WHO), STIs, including HIV and viral hepatitis, account for approximately 2.3 million deaths annually, with more than 1 million new infections occurring daily worldwide [1]. In SSA, the prevalence of STIs remains alarmingly high, particularly among key populations (KPs) such as sex workers, men who have sex with men, transgender people, incarcerated individuals, people who inject drugs, adolescent girls, and young women [2]. These groups are at an elevated risk due to specific high-risk behaviors and structural factors. The region faces significant public health challenges, with STIs like syphilis, chlamedia, and gonorrhea remaining more prevalent, despite increased screening efforts [3,4]. Untreated STIs can lead to severe health complications, including infertility, pelvic inflammatory disease, and an increased risk of HIV transmission [5]. Sexually transmitted infection diagnosis and management in SSA face numerous challenges, primarily due to the region's under-resourced healthcare systems. Current methods include laboratory testing, point-of-care testing, and syndromic management [6]. Syndromic management, which treats symptoms without laboratory confirmation, is commonly used in resource-limited settings but often leads to over- or under-treatment. Non-syrdromic management, involving precise laboratory tests, offers more accurate diagnoses but requires infrastructure that is often lacking [7]. The Fourth Industrial Revolution (4 IR) is characterized by the widespread adoption of advanced technologies, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and data analytics, which promise to improve the diagnosis and management of STIs [8, 9, 10, 11]. These technologies enable more accurate diagnoses and personalized treatment plans, enhancing the quality of care [8,9]. However, their adoption is hindered by challenges, such as limited digital literacy, poor internet connectivity, and inadequate healthcare infrastructure [12,13]. These technologies can process large datasets quickly, enabling early detection and accurate diagnosis of STIs, particularly in KPs, which is critical for effective treatment and prevention efforts [14]. For instance, AI models have been successful in detecting and predicting STI outbreaks, managing treatment plans, and improving patient outcomes in developed countries [15,16]. However, in SSA, the adoption of these technologies faces significant challenges, including inadequate infrastructure, limited access to reliable data, and societal factors, such as stigma and cultural beliefs [17,18]. Despite these obstacles, there have been successful pilot projects in countries like Kenya, Egypt, and South Africa, where AI has been integrated into healthcare systems to some extent [19]. The potential for AI technologies to revolutionize STI management in SSA is clear, but widespread implementation requires overcoming significant barriers [13,20]. This study aims to address the gap in knowledge and implementation of AI technologies in the management of STIs among KPs and general populations in SSA. Despite the high burden of STIs and the potential of 4 IR technologies to improve healthcare outcomes, the region has been slow to adopt these innovations [21]. By exploring how 4 IR technologies can be harnessed to manage STIs more effectively, this study seeks to contribute to the broader goal of achieving Sustainable Development Goal 3 of the United Nations 2030 agenda, which aims to ensure healthy lives and promote well-being for all by 2030 [22,23]. The study will also assess the challenges and successes of early adopters of AI technologies in SSA, providing valuable insights that can inform future strategies for the region [24].