The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets. Methods: A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic. Results: AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies. Conclusions: AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments.

Mpox, formerly known as Monkeypox, is a viral zoonotic disease endemic to Central and West Africa that has posed significant public health challenges since its identification in 1970. Despite decades of experience in managing outbreaks, the 2022–2024 Mpox outbreaks exposed substantial gaps in global preparedness and response, leading the World Health Organization (WHO) to declare a Public Health Emergency of International Concern (PHEIC) in 2022. The resurgence of cases in Europe in 2022 and the more recent emergence of the virulent clade Ⅰb in the Democratic Republic of the Congo (DRC) in 2024 have highlighted a critical need for improved proactive and response strategies to curb the epidemic. This narrative review examines the historical and recent epidemiology of Mpox in Africa and explores the factors that have limited effective management. These include objective influences such as viral mutations, zoonotic transmission patterns, and environmental changes like deforestation, as well as subjective factors, including delayed responses, limited vaccine availability, cessation of smallpox vaccinations, and inequitable access to healthcare. In particular, the review emphasizes the ongoing disparities in global health equity, as wealthier nations have been able to secure vaccines and therapeutics quickly, while endemic regions in Africa continue to struggle with limited resources. The review also discusses how socio-economic and cultural factors, combined with weak public health infrastructure and inadequate surveillance systems, perpetuate cycles of outbreak in vulnerable populations. Furthermore, the emergence of clade Ⅰb in 2024, with its higher virulence and mortality rates among children, particularly in rural areas, underscores the urgency of addressing the evolving epidemiological landscape of Mpox. In response to these challenges, this review recommends strengthening healthcare infrastructure, enhancing surveillance systems, ensuring equitable access to vaccines and treatments, and integrating environmental management into public health strategies. Global collaboration remains essential to provide African countries with the resources and support needed to manage and prevent future outbreaks effectively. Without these measures, the world risks a prolonged public health crisis with far-reaching consequences for both Africa and the global community.

This study aims to assess the knowledge, exposure, and willingness of Nigerian nursing students to integrate artificial intelligence (AI) into healthcare, identifying gaps that may hinder its adoption. AI is revolutionizing healthcare through improved diagnostics, predictive analytics, and automated administrative processes. However, research on AI awareness and adoption among nursing students in low- and middle-income countries (LMICs), particularly in Nigeria, remains limited. Understanding the preparedness of future nurses for AI integration is crucial for optimizing healthcare delivery and education. A cross-sectional study design was employed to evaluate AI knowledge, exposure, and adoption readiness among nursing students in Nigeria. The study involved 676 nursing students from five universities in Nigeria. A structured questionnaire was administered to assess sociodemographic characteristics, AI knowledge, exposure to AI facilities, and willingness to adopt AI technologies. Data were analyzed using descriptive and inferential statistics with Microsoft Excel and JASP 0.19. Associations were tested using ANOVA and Chi-square tests at a 95 % confidence interval (p < 0.05). Findings revealed that 98 % of students had low AI knowledge, with only 1.8 % demonstrating average knowledge and 0.15 % showing high knowledge. Despite this, 85.5 % were willing to take AI training, and 92.6 % believed AI could enhance healthcare workflow. However, concerns regarding inadequate infrastructure (43.5 %) and privacy issues (37.1 %) were prevalent. Significant associations were found between AI knowledge and location, self-perception, and willingness to train (p < 0.001). The study highlights critical knowledge gaps in AI among Nigerian nursing students despite a high willingness for adoption. AI education should be integrated into nursing curricula, and infrastructure improvements are necessary to support AI implementation in healthcare. Addressing these gaps will better prepare nursing students for the evolving landscape of AI-driven healthcare.

Endoscopy remains the gold standard for gastrointestinal (GI) diagnostics, enabling direct visualization and intervention within the GI tract. However, diagnostic accuracy and procedural outcomes vary significantly depending on the endoscopist's skill and experience, leading to potential missed lesions and inconsistent patient care. The integration of artificial intelligence (AI) into endoscopic practice offers a promising solution to address these limitations and enhance diagnostic precision. This review explores the current applications of AI in endoscopy, focusing on image analysis, lesion detection, classification, and workflow optimization, while evaluating the impact on clinical practice and identifying implementation challenges. A literature search was conducted using PubMed, Google Scholar, and IEEE Xplore databases for studies published between January 2010 and December 2024. Keywords included "Artificial Intelligence," "Endoscopy," "Gastrointestinal Diseases," "Image Analysis," and "Lesion Detection." Studies were selected based on their focus on AI applications in endoscopy with quantitative or qualitative data on performance and clinical impact. AI demonstrates exceptional capabilities in polyp detection, achieving detection rates that often surpass those of human practitioners, with systems such as GI Genius showing high sensitivity and specificity. Convolutional neural networks excel in real-time lesion identification and classification, differentiating between benign and malignant growths with remarkable precision. AI also optimizes endoscopic workflows through automated reporting and advanced training tools. Although AI integration shows promise for enhancing endoscopic diagnostic accuracy and procedural efficiency, successful implementation requires careful consideration of current limitations, including reliance on industry-sponsored studies, and addressing challenges in data quality, clinical workflow integration, and regulatory considerations. Future developments in advanced algorithms, personalized medicine, and telemedicine may further advance endoscopic practice and improve patient outcomes.