ABSTRACT
Introduction: A humanitarian crisis means any event or series of events that are harmful to the health or security of the general population. The Niger Republic is currently facing a serious crisis amid the military coup of July 26, 2023. We fear that if nothing is done about it quickly, there could be a civil war in Niger and other neighboring countries. Aim: This present article aims to provide comprehensive knowledge about the ongoing humanitarian crisis in Niger, highlighting the factors that caused it and providing possible solutions for them. Methodology: A literature search was conducted on the recent humanitarian crisis caused by the military coup in Niger Republic using databases such as PubMed, ResearchGate, Google Scholar, World Health Organization, United Nations, online newspapers, and reports from 2013 to 2023. Result: We found that the humanitarian crisis amid the military coup in Niger Republic has further amplified the vulnerability of millions of Nigeriens to more problems in the Niger Republic. The number of Nigeriens in need of humanitarian assistance has reportedly increased by 65%, from 2.3 million in 2019 to 3.8 million in 2021. More than two million children in Niger have been impacted by the crisis in the country and need humanitarian assistance. Furthermore, other causes of the humanitarian crisis in the Niger Republic include sociodemographic factors, climate change, and other natural disasters. Conclusion: The humanitarian crisis in Niger, caused by different factors such as socioeconomic stagnation, environmental causes, food insecurity, terrorist activities, and internal displacement, is an alarming situation that requires an urgent solution and a response from the international community. There is a need to analyze all the underlying causes of humanitarian crises and find long-lasting solutions for them.
ABSTRACT
Background and Aims: Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low- and middle-income countries (LMICs). Methods: In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision-making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies. Results: In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource-constrained LMICs. Conclusion: While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data-sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.
ABSTRACT
This review explores task shifting and task sharing in sub-Saharan African healthcare to address workforce shortages and cost-effectiveness. Task shifting allocates tasks logically, while task sharing involves more workers taking on specific duties. Challenges include supply chain issues, pay inadequacy, and weak supervision. Guidelines and success measures are lacking. Initiating these practices requires evaluating factors and ensuring sustainability. Task shifting saves costs but needs training and support. Task sharing boosts efficiency, enabling skilled clinicians to contribute effectively. To advance task shifting and sharing in the region, further research is needed to scale up effective initiatives. Clear success indicators, monitoring, evaluation, and learning plans, along with exploration of sustainability and appropriateness dimensions, are crucial elements to consider.
