Abstract
Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.
Highlights
Big data, as the term implies, refers to huge volumes of data that are challenging to manage with standard software or web-based systems
The thematic areas identified for discussion included machine learning (ML) and its applications in healthcare; blockchain technology and its application in healthcare; artificial intelligence (AI) and big data analytics in healthcare; personalized health using ML over big data and the internet of things (IoT)
The review findings from the published literature are summarized under various broad subsections with detailed summaries, classifications, and applications of various technologies in healthcare and public health domains
Summary
As the term implies, refers to huge volumes of data that are challenging to manage with standard software or web-based systems. Transaction-level data, video, audio, text, or log files are among the several forms of structured and disorganized data that every company or system may gather. These three ‘Vs’ have become the industry standard for defining big data [1]. Whether in industry or academia, generates and analyzes big data for various objectives. The administration of this massive pile of data, which may be both structured and disorganized, is the most difficult endeavor. We must choose an algorithm depending on the problem to make more accurate forecasts
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