Influential Usage of Big Data and Artificial Intelligence in Healthcare

  • Yan Cheng Yang
    Foreign Language Department, Luoyang Institute of Science and Technology, Luoyang, Henan, China
  • Saad Ul Islam
    Department of Computer Science, University of Swabi, Swabi, Pakistan
  • Asra Noor
    Department of Computer Science, University of Swabi, Swabi, Pakistan
  • Sadia Khan
    Department of Computer Science, University of Swabi, Swabi, Pakistan
  • Waseem Afsar
    Department of Computer Science, University of Swabi, Swabi, Pakistan
  • Shah Nazir
    Department of Computer Science, University of Swabi, Swabi, Pakistan

説明

<jats:p>Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.</jats:p>

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