Artificial intelligence of things

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The Artificial Intelligence of Things (AIoT) is the combination of Artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics. [1] [2] [3]

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In 2018, KPMG published a foresight study on the future of AI including scenarios until 2040. [4] The analysts describe a scenario in detail where a community of things would see each device also contain its own AI that could link autonomously to other AIs to, together, perform tasks intelligently. Value creation would be controlled and executed in real-time using swarm intelligence. Many industries could be transformed with the application of swarm intelligence, including: automotive, cloud, medical, military, research, and technology.

In the AIoT an important facet is AI being done on some Thing. In its purest form this involves performing the AI on the device, i.e. at the edge or Edge Computing, with no need for external connections. There is no need for an Internet in AIoT, it is an evolution of the concept of the IoT and that is where the comparison ends.

The combined power of AI and IoT, promises to unlock unrealized customer value in a broad swath of industry verticals such as edge analytics, autonomous vehicles, personalized fitness, remote healthcare, precision agriculture, smart retail, predictive maintenance, and industrial automation. [5]

Artificial Intelligence Through Medical Devices

As defined by the 21st Century Cures Act in 2016, a medical device is a device that performs a function in healthcare with the intention of using it "in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other animals, or intended to affect the structure or any function of the body of man or other animals". [6]

Under the Federal Food, Drug, and Cosmetic Act, all AI systems falling within this definition are regulated by the FDA. Medical devices are classified into three classes by the FDA based on their uses and risks. The higher the risk is, the stricter the control. The Class I category includes devices with the smallest risk and Class III has the greatest risk. [6] Approved medical devices that utilize artificial intelligence or machine learning (AI/ML) has been increasing steadily. By 2020, the United States The Food and Drug Administration (FDA) approved very many medical devices that utilized AI/ML. A year later, the FDA released a regulatory framework for machines that use AI/ML software, in addition to the EU medical device regulation, which replaced the EU medical. [7] As technology continues to improve, it has rapidly increased the medical fields' method of working and diagnosing. Various AI applications can improve productivity and reduce medical errors, such as diagnoses and treatment selection, and creating risk predictions and stratifying diseases. [8]

AI also helps patients by providing patients' data, electronic health records, mobile apps, and providing easy access to devices and sensors to specific patients who are in need of such technologies. The need to protect patients' data is extreme. Using electronic records to conceal patient data becomes increasingly difficult as data becomes integrated into clinical care. The accessibility to patients' data may be easy to access for the patient, but it also brings skepticism of data protection.

Technology and AI have combined to provide opportunities for better management of healthcare information and technology integration in the medical industry. AI is implemented to recognize abnormalities and suspicion to sensitive data being accessed by a third-party. On the other hand, it will be necessary to rethink confidentiality and other core medical ethics principles in order to implement deep learning systems, since we cannot rely solely on technology. [6]

Artificial Intelligence in Cloud Engineering

When integrating AI into cloud engineering, it can help multiple professional fields in maximizing data collection. It can improve performance and efficiency through digital management.

Cloud engineering follows engineering methods to apply to cloud computing and focuses on technological cloud services. [9] In conceiving, developing, operating, and maintaining cloud computing systems, it adopts a systematic approach to commercialization, standardization, and governance. Among its diverse aspects are contributions from development engineering, software engineering, web development, performance engineering, security engineering, platform engineering, risk engineering, and quality engineering. [10]

Implementing AI into information technology's framework to establish smooth workloads and automate repetitive processes. [11] Using these tools, organizations can better manage data as they develop greater amounts of collective data and integrate data recognition, classification, and management processes as time progresses.

With AI, it can bring efficiency to organizations, bringing strategic methods and saving time from repeated tasks. By executing analysis, organizations can save time and be more efficient.

See also

Related Research Articles

<span class="mw-page-title-main">Biomedical engineering</span> Application of engineering principles and design concepts to medicine and biology

Biomedical engineering (BME) or medical engineering is the application of engineering principles and design concepts to medicine and biology for healthcare purposes. BME is also traditionally logical sciences to advance health care treatment, including diagnosis, monitoring, and therapy. Also included under the scope of a biomedical engineer is the management of current medical equipment in hospitals while adhering to relevant industry standards. This involves procurement, routine testing, preventive maintenance, and making equipment recommendations, a role also known as a Biomedical Equipment Technician (BMET) or as clinical engineering.

<span class="mw-page-title-main">Health informatics</span> Applications of information processing concepts and machinery in medicine

Health informatics is the field of science and engineering that aims at developing methods and technologies for the acquisition, processing, and study of patient data, which can come from different sources and modalities, such as electronic health records, diagnostic test results, medical scans. The health domain provides an extremely wide variety of problems that can be tackled using computational techniques.

<span class="mw-page-title-main">Medical device</span> Device to be used for medical purposes

A medical device is any device intended to be used for medical purposes. Significant potential for hazards are inherent when using a device for medical purposes and thus medical devices must be proved safe and effective with reasonable assurance before regulating governments allow marketing of the device in their country. As a general rule, as the associated risk of the device increases the amount of testing required to establish safety and efficacy also increases. Further, as associated risk increases the potential benefit to the patient must also increase.

Mutual authentication or two-way authentication refers to two parties authenticating each other at the same time in an authentication protocol. It is a default mode of authentication in some protocols and optional in others (TLS).

Health technology is defined by the World Health Organization as the "application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve quality of lives". This includes pharmaceuticals, devices, procedures, and organizational systems used in the healthcare industry, as well as computer-supported information systems. In the United States, these technologies involve standardized physical objects, as well as traditional and designed social means and methods to treat or care for patients.

<span class="mw-page-title-main">Edge computing</span> Distributed computing paradigm

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. Edge computing is an architecture rather than a specific technology, and a topology- and location-sensitive form of distributed computing.

The Internet of things (IoT) describes physical objects with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. The Internet of things (IoT) falls under the Electronics & Communication and Computer Science Engineering. Internet of things has been considered a misnomer because devices do not need to be connected to the public internet, they only need to be connected to a network, and be individually addressable.

Digital health is a discipline that includes digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. It uses information and communication technologies to facilitate understanding of health problems and challenges faced by people receiving medical treatment and social prescribing in more personalised and precise ways. The definitions of digital health and its remits overlap in many ways with those of health and medical informatics.

Fog computing or fog networking, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of computation, storage, and communication locally and routed over the Internet backbone.

<span class="mw-page-title-main">Fintech</span> Subset of technologies used in finance

Fintech, a portmanteau of "financial technology", refers to firms using new technology to compete with traditional financial methods in the delivery of financial services. Artificial intelligence, blockchain, cloud computing, and big data are regarded as the "ABCD" of fintech. The use of smartphones for mobile banking, investing, borrowing services, and cryptocurrency are examples of technologies designed to make financial services more accessible to the general public. Fintech companies consist of both startups and established financial institutions and technology companies trying to replace or enhance the usage of financial services provided by existing financial companies.

A digital twin is a digital representation of an intended or actual real-world physical product, system, or process that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, integration, testing, monitoring, and maintenance. The digital twin has been intended from its initial introduction to be the underlying premise for Product Lifecycle Management and exists throughout the entire lifecycle of the physical entity it represents. Since information is granular, the digital twin representation is determined by the value-based use cases it is created to implement. The digital twin can and does often exist before there is a physical entity. The use of a digital twin in the creation phase allows the intended entity's entire lifecycle to be modeled and simulated. A digital twin of an existing entity may be used in real-time and regularly synchronized with the corresponding physical system.

<span class="mw-page-title-main">Artificial intelligence in healthcare</span> Overview of the use of artificial intelligence in healthcare

Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.

The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.

The Open Artificial Pancreas System (OpenAPS) project is a free and open-source project that aims to make basic artificial pancreas system (APS) technology available to everyone. The OpenAPS project was designed with the idea of quickly getting the APS technology to more people using a direct approach, rather than waiting for clinical trials to be completed and regulatory approval to be granted.

Intelligent transformation is the process of deriving better business and societal outcomes by leveraging smart devices, big data, artificial intelligence, and cloud technologies.

The Internet of Military Things (IoMT) is a class of Internet of things for combat operations and warfare. It is a complex network of interconnected entities, or "things", in the military domain that continually communicate with each other to coordinate, learn, and interact with the physical environment to accomplish a broad range of activities in a more efficient and informed manner. The concept of IoMT is largely driven by the idea that future military battles will be dominated by machine intelligence and cyber warfare and will likely take place in urban environments. By creating a miniature ecosystem of smart technology capable of distilling sensory information and autonomously governing multiple tasks at once, the IoMT is conceptually designed to offload much of the physical and mental burden that warfighters encounter in a combat setting.

<span class="mw-page-title-main">Merative</span> U.S. healthcare company

Merative L.P., formerly IBM Watson Health, is an American medical technology company that provides products and services that help clients facilitate medical research, clinical research, real world evidence, and healthcare services, through the use of artificial intelligence, data analytics, cloud computing, and other advanced information technology. Merative is owned by Francisco Partners, an American private equity firm headquartered in San Francisco, California. In 2022, IBM divested and spun-off their Watson Health division into Merative. As of 2023, it remains a standalone company.

<span class="mw-page-title-main">Coronavirus breathalyzer</span> Diagnostic medical device

A coronavirus breathalyzer is a diagnostic medical device enabling the user to test with 90% or greater accuracy the presence of severe acute respiratory syndrome coronavirus 2 in an exhaled breath. As of the first half of 2020, the idea of a practical coronavirus breathalyzer was concomitantly developed by unrelated research groups in Australia, Canada, Finland, Germany, Indonesia, Israel, Netherlands, Poland, Singapore, United Kingdom and USA.

AI-assisted virtualization software is a type of technology that combines the principles of virtualization with advanced artificial intelligence (AI) algorithms. This fusion is designed to allow more efficient, dynamic, and intelligent management of virtual environments and resources. This novel technology has been employed in a range of industries, including cloud computing, healthcare, data centers, and network infrastructure, to optimize performance, resource allocation, and security protocols.

Eko Health Inc. (Eko) is an American healthcare technology company that develops medical devices, software, and clinical support AI to detect cardiovascular and pulmonary disease.

References

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