Business analytics has undergone significant transformation because of artificial intelligence and machine learning evolution, which now fulfills a critical function in economic expansion and organizational decision-making operations. Organization within the United States economy utilizes Management Information Systems to gain AI-powered insights, which enhance productivity and optimize resources and market prediction accuracy. The research evaluates how AI analytics drive economic growth because they enhance predictions while reducing possible hazards and generate strategic decisions through data-based approaches. The research uses both quantitative data methods together with qualitative case studies to investigate the subject. A combination of secondary market data, business reports, and economic statistics undergoes ML algorithm analysis, which reveals economic patterns and correlations because of their impact on performance. The research obtains practical information and usage barriers from both business managers and policymakers by conducting structured interviews regarding their implementation experiences with AI-based economic decision systems. The paper examines three significant AI methods, including predictive analytics with natural language processing and deep learning, which are applied to business intelligence. Voluntary business analytics, which run on artificial intelligence systems, boost decision-making by generating instant analytic information and simplifying complicated economic analysis tasks. Organizations that integrate AI and MIS systems to build data-based strategies boost operational performance while gaining competitive markets and ensuring durable economic expansion. Organizations should resolve the integration challenges together with data privacy concerns and ethical issues. The research findings demonstrate why organizations need to adopt AI-based analytics systems for developing business resilience and economic innovation in the United States.

In this research, an advanced framework is presented which combines Explainable Artificial Intelligence (XAI), machine learning algorithms and knowledge representation techniques to improve personalized treatment recommendations in healthcare. Random Forest, XGBoost and Deep Neural Networks (DNN) are used in this study to predict optimal treatment plans; thereby, SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) provides means of explaining models. A method is implemented, which uses knowledge graphs and SNOMED CT and UMLS ontologies for structuring patient data and disease-treatment relationships. Thus, the proposed framework is trained and tested on MIMIC-III and eICU Collaborative Research Database, utilizing over 50,000 patient records to assess its performance. The model performance is evaluated using accuracy, F1-score, AUC-ROC and SHAP scores to measure the model explain ability. Results show a 25% improvement in interpretability ratings of healthcare professionals and a 17.6% improvement in predictive accuracy from traditional AI models to state-of-the-art AI models. This study bridges representation gaps of AI driven recommendations and brings it closer to aid in clinical decision-making, improving transparency and trust in AI assisted healthcare. While integrating knowledge graphs and explainable AI techniques can help improve model performance and clinician adoption, using limited human insights to train AIs can perpetuate biased practices and institutions from linear AI. We will continue future research with real world clinical trials and further expand the framework to also utilize multi-institutional datasets for wider application.

The clinical use of artificial intelligence for precision medicine delivers transformative results in drug discovery through its combination of big data analytics and machine learning with genomic research. The traditional methods used in the drug discovery process are expensive to implement and take lengthy durations while failing to deliver customized therapy for patients. The advent of artificial intelligence now conducts fast genomic database analysis to find new drug aims and develop targeted medicine for individual patients. The application of ML algorithms enables researchers to make disease progression forecasts as well as identify drug effectiveness and potential adverse effects leading to improved outcomes for genomic drug discovery patients. The research discusses how deep learning and reinforcement learning models are applied to work with big genomic and biomedical information datasets. Data storage through cloud computing platforms together with high-performance computing systems allows precision medicine to scale and become more efficient. AI computational biology combined with clinical information enhances pharma research by optimally structuring drug reuse analysis and promoting new drug development processes. The use of artificial intelligence leads genomics-based drug discovery to a new direction through high efficiency and cost reduction and individual patient therapy delivery. Precision medicine based on AI and bioinformatics advancements push forward personal healthcare into the future despite ongoing obstacles such as data security problems and interpretation challenges and regulatory limitations.

This study investigates the integration of artificial intelligence (AI) and cybersecurity frameworks in enhancing national resilience through real-time crisis response and critical infrastructure protection. Employing a qualitative case study approach, the research examines twelve carefully selected national and sectoral implementations across diverse contexts, including public health emergencies, smart grid monitoring, intelligent transportation systems, water management, and cyber-physical infrastructure defense. The study reveals that AI-driven data analytics significantly improve early warning capabilities, situational awareness, and decision-making speed in high-risk scenarios. It also demonstrates that the adoption of AI-enhanced cybersecurity tools—such as anomaly detection, behavioral analytics, and autonomous incident response—plays a crucial role in securing digital infrastructure against evolving cyber threats. Furthermore, the application of simulation models and digital twins was found to support real-time modeling, predictive planning, and operational testing, thereby strengthening the adaptability of critical systems. Multi-agent decision support systems and explainable AI interfaces facilitated better interagency coordination and user trust, while zero-trust architectures enabled granular control over access and threat containment. Despite these advancements, the study identified notable gaps in methodological integration, sectoral coverage (particularly in education and water sanitation), and inclusive system design. The findings emphasize the importance of interdisciplinary collaboration and governance alignment in developing comprehensive AI and cybersecurity strategies for national resilience. By synthesizing empirical evidence from twelve cross-sectoral case studies, this research contributes actionable insights into the design and implementation of intelligent, secure, and adaptive infrastructure systems in an era of complex and interconnected global threats.

The construction sector is responsible for damaging the environment, using lots of energy and emitting copious greenhouse gases. As ecological issues grow, projects that use bio-based and recycled materials in buildings are becoming more popular. It gives us better ways to build by lowering the environmental impact and supporting reuse of resources. Using a Life Cycle Sustainability Assessment, this study assesses how these materials perform with regard to environmental, economic and social factors. The industry and process input-output models are included in the framework’s LCSA method, which adds Life Cycle Assessment Life Cycle Costing, and Social Life Cycle Assessment . Existing case studies, EPDs and reports from companies were all sources of data used in this study. The process of economic and social assessment drew from stakeholder surveys along with market data and social hotspot databases. The research demonstrates that using bio-based and recycled materials results in less carbon emissions, less energy being used and less loss of important resources than traditional ones. Bio-based materials tend to be more expensive at first, but they give positive economic and health results over the long run. These materials encourage new job opportunities and are good for the community. The results support the idea that LCSA is essential for making environmentally friendly choices in construction and encourage using circular economy ideas in the building process.

The high price opacity of the United States healthcare persistently weighs down on consumers, as evidenced by studies indicating that more than 65% of patients are surprised by the cost of the treatment after getting care. Finally, it provides a solution to enhanced ‘transparency’ and cost predictability leveraging big data technologies. The analysis of a dataset comprising of more than 5 million hospital billing records from more than 1,200 US hospitals, obtained from the Centers for Medicare & Medicaid Services (CMS) data, in this research helps analyze how big data strategies can improve price transparency. The study employs a data mining technique, regression analysis and visualization tools to quantify price discrepancies exceeding 300% for identical procedures between different regions. Both predictive analytics models were useful in consumer facing pricing tools and were able to predict average procedure costs with an average 92% accuracy. The results verify that big data can revolutionize the process of identifying pricing anomalies and informing regulatory strategies. However, one of the challenges present today is the lack of standardization of data and interoperability. This dissertation presents a scalable framework for bringing big data into national healthcare pricing systems that better advance transparency, equity, and consumer trust.