Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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6248 research outputs found
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AI-Powered RPM Data Platform for Nurse Time Optimization: Reducing Alert Fatigue and Enhancing Efficiency
No full textThis article explores the application of an AI-driven remote patient monitoring (RPM) data platform in optimizing nurse workflows and reducing alert fatigue. By integrating data from wearable devices and electronic health records, the platform utilizes artificial intelligence algorithms to filter and prioritize alerts, ensuring nurses focus on critical patient needs. The article demonstrates how this platform reduced false-positive alerts, enabling nurses to allocate more time to direct patient care and strategic decision-making. The article also emphasizes the platform\u27s scalability and real-time monitoring capabilities, which support proactive interventions and improved patient outcomes while addressing workforce challenges in healthcare settings
AI in Precision Oncology: Revolutionizing Cancer Treatment Through Personalized Drug Discovery
No full textArtificial intelligence (AI) is revolutionizing precision oncology by enabling early detection of cancer, accurate diagnosis, and personalized treatment plans. This research aligns the mechanisms and techniques stemming from AI with their implications towards advancements in cancer treatment, with particular focus on its role in personalization for the sake of precision medicinal diagnoses. AI-powered technologies have revolutionized cancer treatment from genetic profiling and pathology interpretation to predictive analytics and medical imaging analysis. Case examples show how AI can tailor treatments to individual patients, improve diagnostic accuracy, guide optimal treatment and monitor treatment response in real time. The Future of AI in Oncology section delves into potential future advancements in the field, discussing how AI technologies could accelerate drug discovery and development, enhance therapy outcomes, and facilitate greater patient engagement. Ultimately, artificial intelligence is not only reshaping cancer treatment; it is also providing patients worldwide with new hope for greater survival and quality of life. This study highlights some revolutionary frontiers of artificial intelligence, like virtual tumor boards, liquid biopsies, and the speeding of drug discovery processes,” the paper said in part. This article uses a narrative review approach to provide a holistic understanding of the transformative potential that artificial intelligence holds for cancer treatment, highlighting its current significance in cancer care and its future potential
Revolutionizing Retail: How Artificial Intelligence is Transforming the Customer Experience
No full textArtificial Intelligence has fundamentally transformed the retail industry, revolutionizing how businesses interact with customers and optimize operations. From advanced recommendation engines and conversational AI to predictive analytics and computer vision systems, AI technologies are enhancing every aspect of the retail experience. The integration of these technologies enables retailers to deliver personalized shopping experiences, optimize inventory management, improve customer service, and strengthen security measures. Machine learning algorithms and deep neural networks power sophisticated systems that can predict customer preferences, manage real-time interactions, forecast demand patterns, and protect sensitive data. As retailers increasingly adopt AI solutions, the technology continues to evolve, promising even greater advancements in customer experience optimization and operational efficiency
Leveraging Machine Learning and Artificial Intelligence to Revolutionize Transparency and Accountability in Healthcare Billing Practices across the United States
No full textThe rising complexity and opacity of healthcare billing practices in the United States have led to significant financial burdens on patients and insurers. Leveraging Machine Learning (ML) and Artificial Intelligence (AI) offers transformative solutions to enhance transparency and accountability in medical billing. This study explores how AI-driven automation, predictive analytics, and natural language processing (NLP) can detect fraud, optimize claims processing, and ensure compliance with regulatory standards (Ahmed et al., 2022). By analyzing historical billing patterns and identifying discrepancies, AI can mitigate billing fraud, reduce administrative costs, and improve financial efficiency in healthcare systems (Bhardwaj et al., 2023). Additionally, ML algorithms can enhance price transparency by providing real-time cost estimations, thus empowering patients to make informed decisions (Singh & Wang, 2023). Ethical considerations, including bias in AI models and data privacy, are also examined (Chowdhury & Roberts, 2021). The findings underscore the potential of AI and ML in revolutionizing healthcare billing practices, making them more equitable, efficient, and accountable. Future research should focus on refining AI models for fairness, integrating block chain for added security, and developing regulatory frameworks to support AI-driven billing solutions (Lee et al., 2022)
ESG Reporting in Enterprise Financial Systems: Challenges and Innovations
No full textThe integration of Environmental, Social, and Governance (ESG) factors has fundamentally transformed enterprise financial systems, shifting from peripheral considerations to core components of investment decision-making and corporate reporting. As sustainable investing expands globally, financial institutions face significant challenges in standardizing ESG metrics, reconciling regional regulatory variations, and addressing data quality inconsistencies. The evolving landscape reveals a complex ecosystem where traditional financial infrastructure proves inadequate for sustainability data\u27s unique characteristics. Technical solutions emerging to bridge these gaps include artificial intelligence for predictive analytics, blockchain for verification transparency, and natural language processing for unstructured data interpretation. While implementation hurdles remain substantial, organizations deploying integrated technological approaches demonstrate measurable improvements in risk identification, investment performance, and stakeholder engagement. The transition toward comprehensive ESG integration represents a pivotal evolution in financial systems, requiring continued innovation and cross-industry collaboration
Streaming Queries: Enabling Real-Time Elastic Scaling in Modern Applications
No full textThe evolution of streaming queries has revolutionized how organizations handle real-time data processing and elastic scaling in modern computing environments. The shift from traditional batch processing to streaming architectures addresses critical challenges in resource allocation, latency reduction, and system performance optimization. By enabling continuous data processing and immediate response capabilities, streaming queries have transformed how businesses manage dynamic workloads across content delivery, e-commerce, and transportation sectors. The integration of AI-driven optimization and structured streaming techniques has established new benchmarks in processing efficiency, resource utilization, and fault tolerance, fundamentally changing how organizations approach real-time data analytics and decision-making
Lessons from Scaling a Hybrid Cloud Platform: How AI Reduced Operational Costs and Improved Reliability
No full textArtificial intelligence has revolutionized hybrid cloud management by addressing critical challenges in resource allocation, cost optimization, and operational efficiency. Organizations adopting AI-driven solutions have achieved significant improvements in workload prediction, network optimization, and automated scaling capabilities. The implementation of these technologies across various sectors, particularly in retail, demonstrates substantial benefits in cost reduction, performance enhancement, and system reliability. Through structured implementation frameworks and robust governance mechanisms, organizations can maximize the value of AI integration while maintaining necessary human oversight and control. The transformation extends beyond technical improvements, fostering innovation in business processes and enabling organizations to adapt swiftly to changing market demands. The integration of AI in cloud management has also catalyzed the development of more sophisticated approaches to data security, compliance management, and cross-platform resource optimization, establishing new standards for enterprise-scale cloud operations and digital transformation initiatives
AI-Powered Test Case Generation for Regulatory Compliance: Leveraging Generative AI in SAP and Salesforce Environments
No full textAI-powered test case generation represents a significant advancement in regulatory compliance testing for enterprise systems, particularly in highly regulated industries like healthcare and finance. This innovation addresses critical challenges in the traditional compliance testing process, which is characterized by labor-intensive manual efforts, high error rates, and substantial documentation requirements. The integration of generative artificial intelligence with established testing frameworks enables organizations to dramatically reduce test creation time while simultaneously improving requirement coverage across SAP and Salesforce environments. Through automated requirement analysis, sophisticated test case generation techniques, and risk-based prioritization algorithms, organizations can achieve comprehensive compliance with regulations such as SOX and GxP guidelines while significantly reducing operational costs. The technical implementation leverages specialized connectors to SAP modules and Salesforce processes, integrating with existing governance frameworks and automation tools to deliver end-to-end traceability. Continuous learning systems further enhance testing quality through historical data analysis, dynamic updating mechanisms, and intelligent optimization algorithms. The resulting business impact includes substantial improvements in operational efficiency, enhanced compliance coverage, increased adaptability to regulatory changes, and streamlined audit processes, demonstrating exceptional return on investment for early adopters across multiple industry sectors
Building an AI-Ready Data Strategy Using Lakehouse Technology
No full textThis article explores how organizations can leverage Lakehouse technology to build robust AI-ready data strategies. Lakehouse architecture represents a paradigm shift in data management by unifying data lake flexibility with data warehouse reliability. The integration of this technology enables organizations to overcome traditional barriers to AI implementation through unified storage, efficient processing, and comprehensive governance. By examining key components including data ingestion, preparation, metadata management, governance, and scalable infrastructure, the article illustrates how Lakehouse technology establishes a foundation for advanced AI applications like predictive analytics, recommendation systems, natural language processing, and automated decision-making. The article addresses common implementation challenges and provides solutions for data governance, quality assurance, infrastructure scaling, and integration with AI/ML tools, offering organizations practical guidance for transforming their data infrastructure into a catalyst for AI innovation
Innovations in Real-Time Operating Systems (RTOS) for Safety-Critical Embedded Systems
No full textThis technical article explores innovations in Real-Time Operating Systems (RTOS) for safety-critical embedded applications across aerospace, automotive, healthcare, and industrial automation sectors. The discussion covers next-generation RTOS architectures, including mixed-criticality systems, time-triggered architectures, and formal verification approaches that ensure deterministic performance with robust safety guarantees. The article examines emerging adaptive RTOS designs that enable controlled runtime adaptation while maintaining certification compliance, highlighting workload-aware resource management techniques and environmental adaptation mechanisms. Despite promising advancements, significant implementation challenges remain, particularly regarding certification under existing regulatory frameworks and verification of adaptive behaviors. Through case studies in aerospace flight control systems, autonomous vehicles, and medical devices, the article illustrates practical applications of these technologies and their impact on critical industries