Artificial intelligence: a survey on evolution, models, applications and future trends

Journal of Management Analytics, Journal Year: 2019, Volume and Issue: 6(1), P. 1 - 29

Published: Jan. 2, 2019

Artificial intelligence (AI) is one of the core drivers industrial development and a critical factor in promoting integration emerging technologies, such as graphic processing unit, Internet Things, cloud computing, blockchain, new generation big data Industry 4.0. In this paper, we construct an extensive survey over period 1961–2018 AI deep learning. The research provides valuable reference for researchers practitioners through multi-angle systematic analysis AI, from underlying mechanisms to practical applications, fundamental algorithms achievements, current status future trends. Although there exist many issues toward it undoubtful that has become innovative revolutionary assistant wide range applications fields.

Language: Английский

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Applications of artificial neural networks in health care organizational decision-making: A scoping review

PLoS ONE, Journal Year: 2019, Volume and Issue: 14(2), P. e0212356 - e0212356

Published: Feb. 19, 2019

Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of at a reduced cost. Applications ANN diagnosis well-known; however, increasingly used inform health management decisions. We provide seminal review the applications organizational decision-making. screened 3,397 articles from six databases with coverage Administration, Computer Science and Business Administration. extracted study characteristics, aim, methodology context (including level analysis) 80 meeting inclusion criteria. Articles were published 1997–2018 originated 24 countries, plurality papers (26 articles) by authors United States. Types included (36 articles), feed-forward (25 or hybrid models (23 articles); reported accuracy varied 50% 100%. The majority informed decision-making micro (61 between patients providers. Fewer deployed for intra-organizational (meso- level, 29 system, policy inter-organizational (macro- 10 Our identifies key characteristics drivers market uptake guide further adoption this technique.

Language: Английский

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Improving the accuracy of medical diagnosis with causal machine learning

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Aug. 11, 2020

Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims explain patient's symptoms by determining the diseases causing them. However, existing machine approaches are purely associative, identifying that strongly correlated with patients symptoms. We show this inability disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate as counterfactual inference task derive diagnostic algorithms. compare our algorithms standard associative algorithm 44 doctors using test set of vignettes. While achieves an accuracy placing top 48% cohort, places 25% doctors, achieving expert accuracy. Our results causal reasoning is vital missing ingredient for applying

Language: Английский

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Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities

Automation in Construction, Journal Year: 2020, Volume and Issue: 112, P. 103081 - 103081

Published: Jan. 15, 2020

Language: Английский

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The ethics of AI in health care: A mapping review

Social Science & Medicine, Journal Year: 2020, Volume and Issue: 260, P. 113172 - 113172

Published: July 15, 2020

Language: Английский

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