A Brief History of AI: How to Prevent Another Winter (A Critical Review)

Toosi, Amirhosein; Bottino, A.; Saboury, Babak; Siegel, Eliot L.; Rahmim, Arman

doi:10.1016/j.cpet.2021.07.001

Cited by 55 publications

(25 citation statements)

References 35 publications

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“…AI requires a multidisciplinary team effort, large amounts of high-quality data, and a rigorous workflow. These limitations initially frustrated researchers’ high expectations, leading to what has been commonly referred to as the “winter of artificial intelligence”, a period of reduced funding and interest in AI research [ 102 ]; however, in recent years, a series of methodological acquisitions have given new impetus to the enormous potential impact of AI in biomedical imaging, particularly the emergency setting, where it is necessary to complete the transition from a decision-making process based on subjective evaluations to one based on data.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Artificial Intelligence in Emergency Radiology: Where Are We Going?

et al. 2022

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Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

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Section: Challenges and Perspectivesmentioning

confidence: 99%

Artificial Intelligence in Emergency Radiology: Where Are We Going?

et al. 2022

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“…However, these approaches have been entirely unsatisfactory, and have only led to very narrow approaches to AI, and have not led to the ability of AI to develop broad and deep general knowledge about the use of language in the world ( Floridi, 2020 ; Stuart and Peter, 2020 ; Toosi et al, 2021 ). Perhaps the biggest limitation with this approach is that, though context was highlighted early as important, these development were based on narrow and overly simplistic forms of knowledge trees, which do not capture rich contextual structure in the real world.…”

Section: The Limited Success Of Linguistical Semantics and Sematic Lo...mentioning

confidence: 99%

A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge

2022

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Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining the emergence of background knowledge processes. In order to explain background knowledge, we present a non-similarity-based post-Skinnerian theory of human language called Relational Frame Theory (RFT) which is rooted in a philosophical world view called functional contextualism (FC). This theory offers a very different interpretation of how categories emerge through the functions of behavior and through contextual cues, which may be of some benefit to existing categorization theories. Specifically, RFT may be able to offer a novel explanation of how background knowledge arises, and we provide some mathematical considerations in order to identify a formal model. Finally, we discuss much of this work within the broader context of general semantic knowledge and artificial intelligence research.

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“…The foundations of AI can be seen in Isaac Asimov's science fiction works from the 1940s and Alan Turing's groundbreaking work on computing devices during World War-II. 3 The concept of utilizing mathematics, statistics and biometricsbased computers to simulate intelligent behavior and critical thinking was initially thought up by Alan Turing, 3 while John McCarthy was the first to use the term "artificial intelligence" in 1956 to describe the engineering and science of creating intelligent machines. 4,5 In the 1970s, health science research became the driving force for AI innovation with the development of various "expert systems" to aid with scientific and clinical decision-making.…”

Section: Introductionmentioning

confidence: 99%

Role of biostatistics and biometrics based artificial intelligence in human medicare system in 2050

Garg¹,

Bisht²

2023

BBIJ

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Artificial Intelligence (AI) seems to be the third biggest scientific discovery after fire and electricity that have and will change the life of human on this earth. Various applications of AI are based on simple mathematical, biometrical and statistical applications using computer and machine learning (ML). Strong networking is the key of the success of the AI based technology. The world is progressing very fast and it is expected that in 2050, AI based diagnosis and the tele-surgery will play an important role in human health. AI based human medicare system may change the human life and prolong the life expectancy to close to 120years for which the human genome is made for.

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A Brief History of AI: How to Prevent Another Winter (A Critical Review)

Cited by 55 publications

References 35 publications

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Artificial Intelligence in Emergency Radiology: Where Are We Going?

A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge

Role of biostatistics and biometrics based artificial intelligence in human medicare system in 2050

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