Artificial intelligence uses
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Artificial Intelligence Uses: A Comprehensive Overview
AI in Decision Making and Big Data
Artificial Intelligence (AI) has significantly evolved, especially with the advent of Big Data and supercomputing power. AI systems are now crucial in decision-making processes, supporting or even replacing human decision-makers. This integration poses several challenges, including the need for conceptual and theoretical development, effective AI-human interaction, and seamless AI implementation.
AI in Business Value Creation
Organizations are increasingly leveraging AI to enhance business value, driven by the surge in data and computational capabilities. AI technologies offer numerous advantages, such as improved operational efficiency and strategic decision-making. However, businesses often struggle with AI adoption due to a lack of understanding of its value-generating mechanisms. Key enablers and inhibitors of AI adoption, along with the typologies of AI use in organizational settings, are critical areas of focus.
AI in Healthcare and Pharmaceutical Research
AI is revolutionizing healthcare by enabling complex data analysis and improving clinical outcomes. In brain care, AI techniques like artificial neural networks, support vector machines, and random forests are used for diagnosis, surgical treatment, and postoperative assessment. These technologies enhance clinicians' decision-making abilities but require comprehensive data and explainable algorithms for better practical use. Additionally, AI is pivotal in disease diagnosis, personalized treatment, drug discovery, and epidemic forecasting, utilizing deep learning, neural networks, and natural language processing.
AI in Education and Online Learning
AI technologies are transforming education by facilitating online learning and distance education. AI is used for adaptive and personalized learning, recognizing and predicting student behaviors, and enhancing educational data mining and learning analytics. These technologies help customize curriculum and content to meet individual student needs, improving learning experiences and outcomes .
AI in Financial Markets
AI has been extensively applied to stock market trading, focusing on portfolio optimization, market prediction, and financial sentiment analysis. These applications have evolved from basic computational models to sophisticated AI algorithms, continuously gaining research attention and becoming more specific and thorough.
AI in Structural Engineering
In structural engineering, AI offers efficient alternatives to classical modeling techniques, addressing uncertainties and complex problems. Machine learning, pattern recognition, and deep learning are prominent AI methods used to enhance decision-making, reduce error rates, and increase computational efficiency. These methods are particularly useful when traditional testing is not feasible, saving significant time and effort.
AI in Nutritional Science
AI applications in nutritional science are expanding, particularly in medical diagnostics, risk prediction, and therapeutic support. AI methodologies like artificial neural networks, machine learning, and deep learning are used in food composition studies, clinical nutrient intake research, and nutritional epidemiology. These technologies help understand the impact of nutrients on health and disease, potentially leading to personalized dietary systems.
Conclusion
AI's transformative potential spans various domains, from decision-making and business value creation to healthcare, education, financial markets, structural engineering, and nutritional science. While AI offers significant benefits, challenges such as data comprehensiveness, algorithm explainability, and effective implementation must be addressed to fully harness its capabilities. The continuous evolution of AI technologies promises further advancements and innovations across multiple fields.
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