Artificial intelligence application
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Artificial Intelligence Applications: A Comprehensive Overview
AI in Supply Chain Management
Artificial Intelligence (AI) has significantly impacted supply chain management (SCM) by enhancing efficiency and decision-making processes. A systematic review of 150 journal articles from 1998 to 2020 categorizes AI applications in SCM into three main areas: sensing and interacting, learning, and decision-making. The study highlights that while learning methods are gaining momentum, sensing and interacting methods are emerging as a critical area of research. Future studies are encouraged to consider behavioral aspects to further advance AI applications in SCM .
AI in Higher Education
AI in Education (AIEd) is an emerging field with applications in academic support, institutional services, and personalized learning. A review of 146 articles from 2007 to 2018 identifies four primary areas of AIEd: profiling and prediction, assessment and evaluation, adaptive systems and personalization, and intelligent tutoring systems. Despite its potential, there is a lack of critical reflection on the challenges and risks associated with AIEd, and a weak connection to theoretical pedagogical perspectives. Further exploration of ethical and educational approaches is necessary for meaningful integration of AI in higher education .
AI in Business
AI is recognized as a disruptive technology in business, with applications spanning various domains. A comprehensive review of 3780 contributions categorizes AI research into implications, applications, and methods. The study identifies six key topics within these themes, providing a structured overview of current research and emerging trends. This classification helps researchers and practitioners understand how to leverage AI to drive business value effectively .
AI in Power Electronics
AI applications in power electronics cover design, control, and maintenance phases, addressing tasks such as optimization, classification, regression, and data structure exploration. The review discusses four categories of AI: expert systems, fuzzy logic, metaheuristic methods, and machine learning. Despite practical implementation challenges, AI offers significant opportunities for innovation in power electronics .
AI in Stock Market Trading
AI has been extensively applied to financial investment, particularly in stock market trading. A review of 2326 papers from 1995 to 2019 categorizes AI applications into portfolio optimization, stock market prediction, financial sentiment analysis, and combined approaches. The research area continues to gain attention, with increasingly specific and thorough literature emerging .
AI in Brain Diseases
AI has shown promise in the brain care domain, aiding in diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment. Techniques such as artificial neural networks, support vector machines, and random forests are widely used. AI enhances clinicians' decision-making abilities, although challenges remain in data collection and algorithm explainability .
AI in Pharmaceutical and Healthcare Research
AI is revolutionizing healthcare and pharmaceutical research by improving disease diagnosis, digital therapy, personalized treatment, drug discovery, and epidemic forecasting. Technologies like deep learning, neural networks, Bayesian nonparametric models, and natural language processing are pivotal. AI's advancement promises rapid, cost-effective healthcare research and improved public health services .
Conclusion
AI's applications span diverse fields, from supply chain management and education to business, power electronics, stock market trading, brain diseases, and healthcare. Each domain benefits from AI's ability to enhance efficiency, decision-making, and personalized services. However, challenges such as ethical considerations, data collection, and algorithm explainability need to be addressed to fully harness AI's potential. Future research should focus on these areas to ensure AI's responsible and effective integration across various sectors.
Sources and full results
Most relevant research papers on this topic
Artificial intelligence applications in supply chain management
AI applications in supply chain management are advancing, with learning methods gaining momentum and sensing and interacting methods emerging as emerging research areas.
Systematic review of research on artificial intelligence applications in higher education – where are the educators?
AI in higher education research mainly focuses on profiling, assessment, and personalization, but there is a need for more critical reflection on challenges, risks, and ethical approaches.
Leveraging Artificial Intelligence in Business: Implications, Applications and Methods
AI in business can drive value through Implications, Applications, and Methods, offering a clear understanding of its potential and emerging trends.
An Overview of Artificial Intelligence Applications for Power Electronics
AI can enhance power electronics design, control, and maintenance by addressing optimization, classification, regression, and data structure exploration tasks.
Artificial Intelligence Applied to Stock Market Trading: A Review
AI in stock market trading has gained continuous attention and the literature is becoming increasingly specific and thorough, with applications ranging from portfolio optimization to stock market prediction.
Artificial intelligence for brain diseases: A systematic review
AI has the potential to improve diagnosis, planning, and outcome prediction in brain diseases, but requires comprehensive data and explainable algorithms for practical use.
A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks
Explainable artificial intelligence (XAI) is mainly developed for safety-critical domains, with visual explanations being more acceptable to end-users, and more attention is needed for general users in sensitive domains like finance and the judicial system.
Principles of Artificial Intelligence
This book bridges the gap between theory and practice by introducing fundamental AI ideas, covering data structures, operations, and control strategies, suitable for senior or graduate AI courses or individual study.
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