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 applications. 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 a clinical engineer.

<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 model that brings computation and data storage closer to the sources of data. More broadly, it refers to any design that pushes computation physically closer to a user, so as to reduce the latency compared to when an application runs on a centralized data centre.

The Internet of things (IoT) describes devices 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 encompasses 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.

Imaging informatics, also known as radiology informatics or medical imaging informatics, is a subspecialty of biomedical informatics that aims to improve the efficiency, accuracy, usability and reliability of medical imaging services within the healthcare enterprise. It is devoted to the study of how information about and contained within medical images is retrieved, analyzed, enhanced, and exchanged throughout the medical enterprise.

<span class="mw-page-title-main">Samsung SDS</span> South Korean IT company and subsidiary of Samsung Group

Samsung SDS Co., Ltd., Established in 1985 as a subsidiary of Samsung Group, is a provider of Information Technology (IT) services, including consulting, technical, and outsourcing services. SDS is also active in research and development of emerging IT technologies such as Artificial Intelligence (AI), Blockchain, Internet of Things (IoT) and outsourcing in engineering. In 2019, Samsung SDS reported a net profit of 750.4 billion won, an increase of 17.5% year-on-year. The company is estimated to have the 11th most valuable brand among global IT service companies, at US$3.7 billion as of January 2020. Samsung SDS has headquarters in South Korea and eight other overseas subsidiaries, one in America, Asia-Pacific, China, Europe, Latin America, Middle East, India, and Vietnam.

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<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 the application of artificial intelligence (AI) to copy human cognition in the analysis, presentation, and understanding 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 arrive at approximate conclusions based solely on input data.

Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery.

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.

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

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Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. As of 2023, the market for AI hardware is dominated by GPUs.

Artificial intelligence in mental health is the application of artificial intelligence (AI), computational technologies and algorithms to supplement the understanding, diagnosis, and treatment of mental health disorders. AI is becoming a ubiquitous force in everyday life which can be seen through frequent operation of models like ChatGPT. Utilizing AI in the realm of mental health signifies a form of digital healthcare, in which, the goal is to increase accessibility in a world where mental health is becoming a growing concern. Prospective ideas involving AI in mental health include identification and diagnosis of mental disorders, explication of electronic health records, creation of personalized treatment plans, and predictive analytics for suicide prevention. Learning how to apply AI in healthcare proves to be a difficult task with many challenges, thus it remains rarely used as efforts to bridge gaps are deliberated.

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References

  1. ↑ Ghosh, Iman (12 August 2020). "AIoT: When Artificial Intelligence Meets the Internet of Things". Visual Capitalist. Retrieved 22 September 2020.
  2. ↑ Lin, Yu-Jin; Chuang, Chen-Wei; Yen, Chun-Yueh; Huang, Sheng-Hsin; Huang, Peng-Wei; Chen, Ju-Yi; Lee, Shuenn-Yuh (March 2019). "Artificial Intelligence of Things Wearable System for Cardiac Disease Detection". 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). pp. 67–70. doi:10.1109/AICAS.2019.8771630. ISBN   978-1-5386-7884-8. S2CID   198932115 . Retrieved 22 September 2020.
  3. ↑ Chu, William Cheng-Chung; Shih, Chihhsiong; Chou, Wen-Yi; Ahamed, Sheikh Iqbal; Hsiung, Pao-Ann (November 2019). "Artificial Intelligence of Things in Sports Science: Weight Training as an Example". Computer. 52 (11): 52–61. doi:10.1109/MC.2019.2933772. ISSN   1558-0814. S2CID   204818358 . Retrieved 22 September 2020.
  4. ↑ Rethinking the value chain. A study on AI, humanoids and robots - Artificial Intelligence: Possible business application and development scenarios to 2040 (Authors: Angelika Huber-Straßer, Marcus Schüller, Nils Müller, Heiko von der Gracht, Petra Lichtenau, Hannah M. Zühlke). KPMG, 2018, accessed 01 August 2021 via researchgate.
  5. ↑ Goja, Asheesh (22 March 2022). "The Architect's Guide to the AIoT". Cisco Tech Blog. Cisco. Retrieved 22 March 2022.
  6. 1 2 3 Pesapane, Filippo; Volonté, Caterina; Codari, Marina; Sardanelli, Francesco (2018-10-01). "Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States". Insights into Imaging. 9 (5): 745–753. doi:10.1007/s13244-018-0645-y. ISSN   1869-4101. PMC   6206380 . PMID   30112675. S2CID   52011834.
  7. ↑ "Medical device", Wikipedia, 2022-07-26, retrieved 2022-07-31
  8. ↑ He, Jianxing; Baxter, Sally L.; Xu, Jie; Xu, Jiming; Zhou, Xingtao; Zhang, Kang (January 2019). "The practical implementation of artificial intelligence technologies in medicine". Nature Medicine. 25 (1): 30–36. doi:10.1038/s41591-018-0307-0. ISSN   1546-170X. PMC   6995276 . PMID   30617336.
  9. ↑ "Cloud engineering", Wikipedia, 2021-08-27, retrieved 2022-07-31
  10. ↑ "Cloud computing", Wikipedia, 2022-07-30, retrieved 2022-07-31
  11. ↑ "Artificial Intelligence in Cloud Computing". www.datacenters.com. Retrieved 2022-08-05.
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