Update and Acceleration of Health Care Using Artificial Intelligence in Medical Treatments and Diagnostics

AbstractResearchers support the growth of artificial intelligence and similar methods in health and medical care for the purpose of continuously improving processes. By focusing on the growth on data analytics, statistics, applied mathematics, and computer methods including machine learning, the future of health-care methods will change. The development of computerized methods and the growth of data systems produce ample materials for artificial intelligence to develop and to bring physician assistance programs to enable continuous improvement resulting in superior health and medical care. This includes applications in intensive care as well as diagnostic therapies. The focus is on examples in the use of the promising developments in data science methods, the accumulation of medical and research data. With quality and continuous improvement in process control applications where one determines the usefulness of data analytics, there are great possibilities of change in the improvement in medical applications as well as the management of medical and health-care treatment and diagnostic facilities.

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Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study

Journal of Medical Internet Research ◽

10.2196/15511 ◽

2019 ◽

Vol 21 (11) ◽

pp. e15511 ◽

Cited By ~ 3

Author(s):

Bach Xuan Tran ◽

Son Nghiem ◽

Oz Sahin ◽

Tuan Manh Vu ◽

Giang Hai Ha ◽

...

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Precision Medicine ◽

Data Science ◽

Latent Dirichlet Allocation ◽

Developed Countries ◽

Theory And Practice ◽

Clinical Practices ◽

Research Topics ◽

Dirichlet Allocation

Background Artificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. Objective This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. Methods We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. Results The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. Conclusions The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

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Big Data Analytics in Supply Chain Management

Encyclopedia of Organizational Knowledge, Administration, and Technology - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-7998-3473-1.ch169 ◽

2021 ◽

pp. 2443-2457

Author(s):

Nenad Stefanovic

Keyword(s):

Big Data ◽

Supply Chain ◽

Supply Chain Management ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Current Approach ◽

Data Systems ◽

Chain Management ◽

Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

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The Nature of Intelligent Analytics

Advances in Data Mining and Database Management - Intelligent Analytics With Advanced Multi-Industry Applications ◽

10.4018/978-1-7998-4963-6.ch001 ◽

2021 ◽

pp. 1-21

Author(s):

Zhaohao Sun ◽

Andrew Stranieri

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Research And Development ◽

Business Intelligence ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Business Analytics ◽

Novel Approach ◽

Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

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A Review of Artificial Intelligence Methods for Data Science and Data Analytics: Applications and Research Challenges

2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on ◽

10.1109/i-smac.2018.8653670 ◽

2018 ◽

Cited By ~ 2

Author(s):

C V Krishna ◽

H R Rohit ◽

Mohana

Keyword(s):

Artificial Intelligence ◽

Data Analytics ◽

Data Science ◽

Research Challenges ◽

Artificial Intelligence Methods

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Big Data Analytics

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2017040103 ◽

2017 ◽

Vol 11 (2) ◽

pp. 41-56 ◽

Cited By ~ 5

Author(s):

Yingxu Wang ◽

Jun Peng

Keyword(s):

Big Data ◽

Cognitive Processes ◽

Data Analytics ◽

Data Science ◽

Computational Models ◽

Big Data Analytics ◽

Data Systems ◽

Data Engineering ◽

Big Data Systems ◽

Cognitive Foundations

Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.

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Intelligent Big Data Analytics

Advances in Data Mining and Database Management - Managerial Perspectives on Intelligent Big Data Analytics ◽

10.4018/978-1-5225-7277-0.ch001 ◽

2019 ◽

pp. 1-19 ◽

Cited By ~ 1

Author(s):

Zhaohao Sun

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Big Data ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Business Decision ◽

Technology System ◽

System Service ◽

Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

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Formal Methods, Artificial Intelligence, Big-Data Analytics, and Knowledge Engineering in Medical Care to Reduce Disease Burden and Health Disparities

Big Data Analytics - Lecture Notes in Computer Science ◽

10.1007/978-3-030-04780-1_21 ◽

2018 ◽

pp. 307-321 ◽

Cited By ~ 1

Author(s):

Sakthi Ganesh ◽

Asoke K. Talukder

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Health Disparities ◽

Medical Care ◽

Formal Methods ◽

Data Analytics ◽

Disease Burden ◽

Knowledge Engineering ◽

Big Data Analytics

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Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated Based Approach

Data Science and Data Analytics ◽

10.1201/9781003111290-1-2 ◽

2021 ◽

pp. 3-18

Author(s):

Sumika Chauhan ◽

Manmohan Singh ◽

Ashwani Kumar Aggarwal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Analytics ◽

Data Science

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Challenges and Applications of Data Analytics in Social Perspectives

Advanced Deep Learning Applications in Big Data Analytics - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-2791-7.ch003 ◽

2021 ◽

pp. 51-67

Author(s):

Venkatesan Manian ◽

Vadivel P.

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Science Field ◽

Research Perspective ◽

Social Perspectives ◽

Programming Tools ◽

Modern Era

This chapter introduces data science with its history and importance in this modern era briefly. This chapter also elaborates the discussion by relating data science to various modern fields like big data analytics, artificial intelligence, deep learning, and machine learning. This chapter also discuss the necessary of data analytics in this big data era. This chapter also briefly introduces another emerging field, Internet of Things (IoT) and explores the contribution IoT towards big data analytics and data science in research perspective. It also briefly introduces the programming and non-programming tools used in the data science field.

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