Revolutionizing Remote Patient Monitoring with AI and IoT (Published)
Amidst the growing trend of chronic disease and the need for continuous, longitudinal care focused on the patient, Remote Patient Monitoring (RPM) systems have been on the rise. This research aims to assess the effectiveness of Artificial Intelligence (AI) and the Internet of Things (IoT) in addressing the efficiency, sensitivity, and generalizability of RPM systems. This research is qualitative and quantitative in nature, utilizing biological real-time signals from publicly available datasets (MIT-BIH, MIMIC-III, Fitbit), employing AI methodologies (Random Forest and Convolutional Neural Network (CNN)) for classifying and predicting anomalies. The proposed edge-enabled Internet of Things architecture lowers latency by 35%; CNNs achieve 93.2% accuracy in electrocardiograms (ECG) classification. Qualitative subject-matter expert responsiveness from healthcare professionals noted a 40% increase in timely intervention for detected anomalies—with confidence in the usability of the systems. Findings advocate AI and IoT enhancements for smart real-time monitoring of health-related information.
Keywords: Convolutional Neural Networks (CNN), Edge Computing, Healthcare Informatics, Internet of Things (IoT), IoMT, Physiological Signal Analysis, Remote Patient Monitoring (RPM), Smart Wearables, artificial intelligence (AI), predictive analytics
The Role of Retrieval-Augmented Generation (RAG) in Financial Document Processing: Automating Compliance and Reporting (Published)
The rapid digitalization of the financial sector has also increased the usage of artificial intelligence (AI) in operations, compliance, and regulatory reporting. Retrieval-augmented generation (RAG) is turning out to be a very prominent AI-driven approach that synergizes retrieval-based and generative models to deliver far better accuracy and efficiency in processing financial documents. Traditional methods for compliance reporting are manual, excruciatingly slow, and vulnerable to human errors, thereby creating a burden of regulatory scrutiny and monetary penalties. By using the power of RAG, financial institutions would automate the encapsulation of relevant information, summarize the sheer volume of regulatory text, and be in a real-time position to comply with ever-changing regulations: IFRS, Basel III, and GDPR. RAG would also provide forensic examination and disparate pattern detection in support of fraud, risk, and due diligence. This paper investigates the role of RAG in the automation of compliance and reporting processes pertaining to financial document processing. It addresses the regulatory compliance challenge, the drawbacks of the traditional document processing approach, and the merits of an AI-based automated approach. A qualitative study of those case studies and industry applications will prove the proposition that RAG enhances financial workflows through lower manual effort, higher data accuracy, and improved decision-making. The paper also discusses strategies for implementation in the context of financial institutions and provides insights into the developments in AI regulation in the future. With the growing embrace of AI-powered alternatives in the financial industry, RAG is an opportunity for game-changing transformation toward optimizing compliance reporting, actualizing risk mitigation, and driving operational efficiencies amid the complexity brought on by the regulatory environment.
Keywords: AI implementation, AI in finance, Fraud Detection, Risk Management, artificial intelligence (AI), compliance automation, compliance monitoring, financial document processing, financial institutions, financial technology (fintech), regulatory reporting, retrieval-augmented generation (RAG)