History, current status, and future directions of artificial intelligence

Kubassova, Olga; Shaikh, Faiq; Melus, Carlos; Mähler, Michael

doi:10.1016/b978-0-12-820239-5.00002-4

Cited by 13 publications

(8 citation statements)

References 263 publications

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“…In the blockchain environment, every node contains a copy of local data called a block. When the nodes agree on a contract, the transaction is performed and has validity [23].…”

Section: Related Workmentioning

confidence: 99%

Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

Reegu

Abas

Hakami

et al. 2022

Security and Communication Networks

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A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase’s research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges.

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“…In the blockchain environment, every node contains a copy of local data called a block. When the nodes agree on a contract, the transaction is performed and has validity [23].…”

Section: Related Workmentioning

confidence: 99%

Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

Reegu

Abas

Hakami

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The AI Silecosystem can trace its origins back to a number of key societal institutions that include commercial enterprises [298][299][300][301][302][303][304], the military [304][305][306][307] and, arguably, most prominently, academic centers [308][309][310]. Given their focus on research and education as well as their often substantial financial resources, academic centers became the natural home, incubator and accelerator of the AI Silecosystem.…”

Section: The Academic Origins and Catalysis Of The Ai Silecosystemmentioning

confidence: 99%

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

McDonnell

2023

JCM

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Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

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“…AI has come a long way since it was first coined by John McCarthy in 1956 at a workshop held at Dartmouth College, where he invited a group of researchers to discuss the possibility and challenges of creating machines that can think [3]. The research on AI eventually stalled in 1974 when funding from the United States and the British governments became highly selective, but it was revived seven years later through the initiative of the Japanese government to fund AI projects [3]. However, the funding was restricted again in the late 1980s as investors were unsure where the research was heading.…”

Section: Introductionmentioning

confidence: 99%

“…However, the funding was restricted again in the late 1980s as investors were unsure where the research was heading. The breakthrough in machine learning in the 2020s spurred interest and investment in AI [3]. AI has since undergone tremendous development in line with the availability of powerful computer hardware and new data management systems.…”

Section: Introductionmentioning

confidence: 99%

Implications of Artificial Intelligence for Teaching and Learning

Tang

2024

Acta Pedagogia Asia

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Artificial Intelligence (AI) has significantly transformed teaching and learning, facilitating a shift from teacher-centered to student-centered education. This review outlines the broad implications of AI for education and synthesizes both the opportunities and challenges associated with its implementation. Examining over 55 papers related to the impacts of AI on education, the review encompasses various educational contexts, avoiding a singular focus on specific types of education or the teaching of AI alone. According to the review, AI introduces new opportunities for creating intelligent content that enhances learning experiences, fostering interactivity and a student-centered approach. Smart content enables instructors to integrate multimedia, interactive tools, AI-related wearables, and information technologies, diversifying learning modes and engaging students more effectively. The creation of smart content aligns with smart education frameworks to ensure efficient content development. AI also contributes to the development of intelligent tutoring systems, which simulate human tutors to deliver personalized and adaptive educational experiences. These systems can host smart content, enabling independent learning. Additionally, AI improves virtual learning environments by analyzing student data to tailor content and delivery methods based on individual needs. It automates tasks such as grading and feedback, allowing teachers to concentrate on other essential responsibilities. While AI brings significant benefits, it is not without limitations. Challenges include infrastructure requirements, considerations of inclusion and equity, teacher readiness and preparation, data quality and inclusivity, profit orientation, data privacy and ethical concerns, and the potential for unequal access. Addressing these limitations is crucial for maximizing the positive impacts of AI in the realm of education.

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History, current status, and future directions of artificial intelligence

Cited by 13 publications

References 263 publications

Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Implications of Artificial Intelligence for Teaching and Learning

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