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acquisition of high quality images and their storage from the many screens associated with every patient (Belle et al., 2015). Physiological signals not only create information dimension problems but also have baffling complexity of a spatiotemporal nature. Nowadays, numerous heterogeneous and uninterrupted monitoring devices are employed in the health care system to apply solitary physiological waveform information or crucial discrete data provided to systems if there should be an occurrence of plain occasion (Cvach, 2012; Drew et al., 2014).

      The human genome is comprised of about thirty thousand genes. It has been observed that the price to sequence the human genome decreases with the advancement of high‐throughput sequencing technology (E.S. Lander and et al., 2001; Drmanac et al., 2010). Investigating genome‐scale information with suggestions for current public fitness insurance policies, the conveyance of care, and creating noteworthy proposals in an opportune way is a sizeable undertaking to the discipline of computational biology (Caulfield et al., 2013; Dewey et al., 2014). In a clinical setting, the delivery of these recommendations are very costly as time is very crucial.

In spite of huge expenditures by the current health care systems, clinical results become minimal (Oyelade et al., 2015). For big data analytics, it is a hazard to expect an extra quintessential section to help with investigation and revelation measures, bettering the conveyance of care, helping with the plan and design of medical care strategy, and make use of a way to exhaustively assess the muddled and tangled medical services information. More specifically, appropriation of the bits of information received from big data analytics can possibly save lives, enhance care conveyance, prolong admittance to clinical services, regulate installment to execution, and assist managing the improvement of clinical offering costs (Belle et al., 2015).

1.3 AI in Healthcare

      Artificial intelligence impacts the health care domain as AI is the development of computer systems able to perform tasks that requires human intellect. Tasks such as object detection, decision making, solving complex problems, and so on are a few main benefits of artificial intelligence. AI also gives us predictions with an increased level of accuracy, it helps in decision making processes, it has solved complex problems, and it quickly performs high‐level computations that take days for a human to solve. AI is something that makes human lives easier by performing high level computations and solving complex problems.

      According to the PricewaterhouseCoopers (PwC) report, artificial intelligence will contribute an additional $ 15.7 trillion to the world economy by 2030, and the greatest impact will be in the health care sector (pwc). Healthcare is getting more import and using AI in more advanced manner. The sudden importance of AI in the health care industry can be categorized into two major points. First, the high availability of medical data; many of us have tons and tons of medical data in the form of our medical history and the availability of data makes implementing artificial intelligence much easier (Bush, 2018). Second, the introduction of complex algorithms. Machine learning alone is not capable of handling high dimensionality data and medical histories are extremely high dimensional in character, there are thousands of attributes that are hard for humans to analyze and process data through machine learning. However, when neural networks and deep learning were introduced, the process become much easier. Neural networks and deep learning are focused on solving complex problems that involve high dimensionality data; their development played a significant role in the impact of AI on health care (Simon et al., 2007; Loria).

      AI benefits health care organizations by implementing cognitive technology to unwind a huge amount of medical records and perform power diagnosis. For instance, Nuance is a production service provider that uses artificial intelligence and machine learning to predict the intent of a particular user. By implementing Nuance in organization system to develop a personalized user experience, a company can make better actions that enhance customer experience and overall benefit the organization. Nuance helps in the storing, collecting, and reformatting of data to provide faster and more consistent access to allow further analysis or diagnosis. These are examples of how AI is gaining attention and being helpful to the health care industry (Aronson and Rehm, 2015; Schmidt‐Erfurth et al., 2018).

1.4 Cloud Computing in Healthcare

There are numerous advantages and benefits of cloud computing in health care as it stores data on demand and reduces operational costs for health care providers such as hospitals and clinics (Rostrom and Teng, 2011). The cloud also supports electronic health records (EHRs), mobile applications that are used for medical concern applications. The cloud has made access to patient's medical history easier for doctors, and patients can now have a clear, in depth track of their medical process as well as track their appointments, results, and reports. Traditional EHRs need a team of medical staff, physicians, medical administrator, and IT members for their smooth processing and management; however, cloud EHRs do not require highly skilled IT members or professionals to develop and manage the system. Cloud EHRs make it easier to access data from anywhere than traditional EHRs system. Cloud computing works on either a one‐time or monthly payment method and is less time consuming, whereas tradition EHR systems are costly and time‐consuming. Cloud EHR systems also ensure that data are always kept safe in the cloud and can be retrieve when required (Mell and Grance, 2011).

      Cloud Computing is significant in the health care sector as it supports big data analytics that are being used to improve decision support systems and contribute to therapeutic strategies in beneficial ways.

1.5 IoT in Healthcare

The internet of things is revolutionizing health care (Keh et al., 2014; Santos et al., 2014; Amendola et al., 2014). As patients become more connected and generate more data, clinicians can identify and address their needs more efficiently than ever before. And with advances in data science and artificial intelligence, the potential for personalized preventative care and other innovations is limitless in health care. In addition, IoT wearable devices help monitor patients' conditions with sensors for body temperature, heart rate, blood pressure, breathing, sleep, and much more that will help doctors or physicians know the medical and lifestyle history of an individual as well as detect early signs of critical diseases. The IoT saves time as well as money and when both things are saved, a life could be saved. For instance, if a person becomes erratic in the middle of night or is alone and experiences symptoms like shortness of breath, heart palpitations, and a sudden change in heart rate or blood pressure, the IoT wearable device will sense the rapid change, record it electronically, and transmit the data to medical providers. Back at the hospital, doctors could already be monitoring and evaluating the data.The nearest clinic or hospital could then provide an ambulance, and before the patient reaches the hospital, all the necessary equipment could be prepared for quick action. In this way, a doctor can save the life of a patient because the IoT was communicating with the health care system and by providing real‐time monitoring to doctors, saved time, money, and effort. In this way, the IoT is transforming health care in a beneficial way (Lee, 2014; Yang et al., 2014; Diogo et al., 2014).

1.6 Conclusion

      Smart health care informatics involve the combined use of the IoT, big data analytics,cloud computing, and artificial intelligence. These technologies will be made of use in health care by the application of artificial intelligence to examine and fit a giant quantity of data to screen for exclusion standards and decide the most appropriate objectives, keeping the time of recruiting the topics, and enhancing the concentrated efforts on the goal population. Further, patients are supervised in actual time with the usage of smart wearable devices to acquire extra time‐sensitive and correct information, for instance, the practice of smart devices to display data in lung ailment clinical trials. Using big data analytics and synthetic genius in the field of health care research and the improvement of drugs will grow to be of greater convenience. The arrival of smart health care, mature standards, and structures has been organized. However, with the upcoming modern technologies, there is a huge scope for development, and many challenges are now coming out.

References

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