Artificial Intelligence (AI) is rapidly transforming healthcare sector by improving diagnostic accuracy, enhancing drug discovery and pharmaceutical research, and advancing electronic prescription processing (e-prescribing; eRx), although its adoption in developing contexts remains limited. This study the role of artificial intelligence in transforming clinical practice and biomedical research: a review of opportunities and challenges presents systematic review of 20 peer-reviewed articles examining the role of AI in clinical practice and biomedical research. The key findings show that AI significantly improves diagnostic precision, supports personalized medicine, enhances real-time patient management system, and optimizes medication management through intelligent e-prescribing systems with accuracy of perception of drugs for each patient. Additionally, AI contributes to faster and more efficient biomedical research processes. However, challenges such as inadequate infrastructure, high implementation costs, data privacy concerns, and limited technical expertise persist, particularly in developing country. The review concludes that effective and sustainable AI integration in healthcare requires context-specific strategies, supportive policy frameworks, increased investment in digital infrastructure, and capacity building among healthcare professionals.
Muhammad Sadisu Isah, Musbahu Salisu, Eli Adama Jiya· Journal of Basics and Applie...· 0 citations
The widespread adoption of microservices architectures on Kubernetes has introduced significant challenges in resource management, particularly the inadequacy of default load balancing under dynamic workloads and issues with pod resource sharing under contention. This paper proposes an integrated auto-scaling platform that combines the Horizontal Pod Autoscaler (HPA), Metrics Server, and Prometheus to dynamically optimize resource utilization in a Kubernetes-in-Docker (KIND) cluster. The experimental platform was deployed on an Ubuntu 22.04 host with a three-node KIND cluster (one master, two workers), using Kubernetes v1.27.3 and Docker v24.0.7, with performance evaluated through CPU utilization and requests per second (RPS) metrics collected via Prometheus and visualized in Grafana. Results demonstrate that HPA effectively responds to workload increases by provisioning additional pods, maintaining system stability and throughput during high-demand periods, with CPU usage and RPS exhibiting predictable scaling behavior aligned with the 15-second Metrics Server scraping interval. The novelty of this work lies in the systematic integration of HPA with Prometheus custom metrics within a KIND environment, extending evaluation across multiple lightweight Kubernetes distributions including microk8s and minikube. This approach enhances scalability, computational efficiency, and cost-effectiveness for microservice-based systems.
Shamsuddeen Rabiu, Sani Muhammad Tanko, Eli Adama Jiya· Journal of Basics and Applie...· 0 citations