Artificial Intelligence and Internet of things are hot subjects today, for that reason, integrating AI into IoT is becoming a frequent practice.

The health care system is everyone’s business, so finding one’s way around is equally important for everybody. Yet, keeping every detail in mind is no simple task. There are limitations to human physical and mental performance. Thus going beyond the maximum needs to be relegated to these technologies as the Internet of things and Artificial Intelligence. The implementation of innovative solutions in health care is always a fantastic idea and IoT together with AI are powerful drivers of the digital transformation whatever field the technology are applied in.

Municipal infrastructure, smart houses, retailing, manufacturing, supply chain, schooling, healthcare and life sciences — the whole digital ecosystem, an IoT ecosystem of connected devices, has been created and is growing stronger with each passing day. Empowered with Artificial Intelligence and Machine Learning, among other things, IoT is employed as a way of equipping people with intelligent aid. Gradually, it’s taking over both small and major processes in numerous industries. Healthcare is no exception.

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Why combine IoT and Artificial Intelligence?

It works both ways — IoT and AI need each other. The Internet of things suggests handling very large volumes of information that must be made sense of and put to work. Therefore, the IoT-related works can and should be improved by AI algorithms to produce the experiences truly significant for customers and/or clients. Well, what type of meaning does AI supply to IoT?

Since IoT is a young technology that joins the gazillions of smart devices, it will have imperfections. As an example, such criteria as precision and rate of IoT data transmission are yet to be improved. Additionally, an artificial intelligence system not only imitates the human means of performing tasks but it’s also learning from what it patterns itself from. This mechanism of self-improvement is of the essence to AI. Speaking in general terms, Artificial Intelligence has much for IoT to benefit from. In the narrow sense, it’s applied as the AI software embedded inside IoT apparatus and augmenting fog or edge computing solutions to bring intelligence to IoT. Because of this, smart devices create such a enormous amount of quickly analyzed sensor information it can’t help but gas Machine Learning increasing the intelligence of the physical things.

Artificial intelligence and IoT in health care

When it comes to combining AI and IoT in health care, odds are collectively they will enhance operational efficiency in this area. Tracking (collecting), tracking (assessing ), management, optimization (training), and automation (modeling, forecasting ) — these are the crucial actions that provide for the intelligent and effective application of AI algorithms in IoT devices.

Acting in concert, they can decrease the load of administrative work for clinical personnel. Having clinical workflows enhanced, medical officers will have the ability to spend more time with the patients and the health care service-delivery is consequently bound to take a longer patient-centric approach.

Therefore, the major use cases of AI-enabled IoT would be the next:

  • Medical personnel, patients, and stock tracking
  • Chronic disease management
  • Drug management
  • Emergency room wait time reduction
  • Remote health control

IoT operational principles in the health care field

Longing to know what the results of such an innovative approach might be is sensible, albeit non-essential. The best way to describe why one ought to utilize AI-enabled IoT in health care is to supply a more thorough evaluation of the IoT operational principles enabling a more nuanced comprehension of possible areas of its application in the medical system.

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1. ) Devices with physical interfaces to/from the real world.

Basically, in health care, any service a customer requests is linked to the physical universe. Besides, the usage of services entails the physical interaction of health professionals, patients, and the apparatus as such. These connected devices as robotics in medication interact with the physical environment through multiple physical interfaces. Developing intuitive physical interfaces with different communication technologies (i.e. Bluetooth, NFC, WiFi, USB) is consequently bound to accelerate the quality of the interaction, lead to a better information flow among IoT robots and enhanced service representation and monitoring for people. The reason is that, given an IoT-based solution, the physical interfaces can create wireless networks. Apparently, the IoT nodes which stand behind specific sensor devices are being grouped together in this way to get devices readily linked to the Internet structure and to find exchange processes well-established.

2. ) Structured data input through detectors.

IoT technology generates wireless sensor networks. As stated before, these networks are successfully bridging the physical and electronic worlds. What prevents the functioning of the data exchange between both of these worlds from becoming a mess is the structured data flow. Input data is collected by the detector apparatus and delivered to the information control center for additional feedback. There could be several data channels included in parallel, nevertheless, multiple sensor data collection is implemented with minimal latency. Also, regardless of the fact that the amount of information sources is typically enormously large, the data records remain small and well-structured as a result of advantage analytics while high dynamism of IoT devices allows deducing missing data in the neighboring gadgets to fill the openings.

3. Tiny input/output devices.

There are certain requirements for the appearance and dimensions of the physical IO devices. Additionally, the needs must be adjusted to the environmental conditions in which the unit is operating. Therefore, in comparison to human ports that need relatively major input/output apparatus, physical ports of IoT devices get input through detectors (which are tiny because of using micro-electromechanical systems (MEMS) technologies ) and send information back to mobile/cloud computers via wired or wireless interfaces. Consequently, there’s not just a demand in, but there’s also a real possibility of getting the size of detectors in addition to the IO devices decreased. Consider such cases as implanted heart-rhythm monitors and apparatus continuously monitoring and measuring biochemical data.







4. ) Human-machine-environment system drivers.

Apparently, human-machine interaction refers to the process of communicating between individuals and automated systems. However, the Web of things is built on the foundation of a much more intricate relationship among the human, machine, and environment. In health care, the environment is an extremely significant parameter that suggests the quantity of physical and social variables; and the real time monitoring of the environmental element is even more crucial for this industry when the IoT technology is in operation.

IoT detectors, the majority of which are wireless, are joining small communities of apparatus. However, without considering all three variables, there’s absolutely not any point in this connection. An individual should not then ignore the fact that IoT systems are created by people and for people to be applied in some specific contexts. Consequently, the physiological and psychological conditions, emotions, actions, and goals of a person — IoT data generated by multi-modal detectors — ought to be, consequently, used as inferrable information and made sense of relying upon the forces of Artificial Intelligence.

5. ) Real-time action and decision management.

One of the primary benefits the interoperability of AI and IoT is bringing is that there’s a opportunity to keep track of what’s happening and to respond upon it on the spot. It means the change toward active patient participation, customized treatment plans real-time modification and a more intelligent approach to information management. Real-time analysis is only possible when a flow of data is constant. However, it’s barely possible that a system doing complicated processing can deal with the nearly continuous data stream from multiple detectors. It’s a real time AI system that can reduce the quantity of information and enable intelligent data management.

Finally, embedded AI technology will be of overriding priority for IoT systems, particularly in health care. Importantly, the desired action here is making sense of this information in the border, near the apparatus, with the advantage and fog computing. Again, data analysis performed in the border rather than a centralized place (i.e. a cloud server or a data centre ) enables a close real-time evaluation right on the IoT devices. Broadly , AI algorithms’ processing information from several IoT smart sensors on the border suggests advancement of monitoring and maintenance.

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Healthcare cybersecurity challenges with IoT

Despite all of the evident benefits that using IoT is providing, the technology can also be challenging the data safety, which is an essential part of the medical infrastructure. The point here is that the unstructured data residing outside organized databases (i.e. electronic records and records ) is most difficult to organize with the support of classic algorithms and so to protect. It’s the powerful learning algorithms which are most likely to contribute much to solving the issue with data analysis. However, having data organized doesn’t mean using it protected from cyber threats.

Thus, the ideal step one can take is to drive standards around IoT ecosystems. IoT devices and applications are often programmed to get both private and sensitive information vulnerable before malware. In light of this, the privacy rule mentioned in the Health Insurance Portability and Accountability Act (HIPAA) will protect such health advice and these regulations as the EU General Data Protection Legislation (GDPR) will obligate the offenders to carry monetary penalties for data manipulation. Managing multiple complex systems through AI-enabled IoT will without doubt create advanced experiences for all involved in the medical system. However, health care must be delivered in a secure way.


Billions of connected devices are generating a great deal of medical sensor information. Finally, an improvement of information organizing processes is required. The use of different kinds of artificial intelligence as innovative predictive calculations is likely to create smarter environments where human-machine interaction will become more safe and efficient. Incorporation of IoT into the procedure for management of healthcare business everyday operations is the reality. Personalized client-oriented service-delivery equipped with the forces of the Internet of things, Artificial Intelligence, advantage, and fog computing is the intention to be attained in the course of time.

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