scholarly journals Artificial Intelligence Publication Trends in Reproductive Cancers - Who is being Left Behind?

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Chan Joshua E ◽  
Liao Cheng-I ◽  
Man Amandeep ◽  
Kapp Daniel S ◽  
Mysona David Pierce
Keyword(s):  
Artificial Intelligence ◽  
Publication Trends ◽  
Left Behind ◽  
Reproductive Cancers

Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients

2021 ◽  
pp. 1-6
Author(s):  
Jacob R. Morey ◽  
Xiangnan Zhang ◽  
Kurt A. Yaeger ◽  
Emily Fiano ◽  
Naoum Fares Marayati ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Outcomes ◽  
Warning System ◽  
Large Vessel ◽  
Time Interval ◽  
Large Vessel Occlusion ◽  
Time Intervals ◽  
Vessel Occlusion ◽  
Left Behind ◽  
Stroke Center

<b><i>Background and Purpose:</i></b> Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes. <b><i>Methods:</i></b> A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. <b><i>Results:</i></b> The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; <i>p</i> = 0.01) with less variation (<i>p</i> &#x3c; 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (<i>p</i> = 0.15). <b><i>Conclusions:</i></b> Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

scholarly journals Urinary chemical fingerprint left behind by repeated NSAID administration: Discovery of putative biomarkers using artificial intelligence

PLoS ONE ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. e0228989
Author(s):  
Liam E. Broughton-Neiswanger ◽  
Sol M. Rivera-Velez ◽  
Martin A. Suarez ◽  
Jennifer E. Slovak ◽  
Pablo E. Piñeyro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chemical Fingerprint ◽  
Left Behind

Sa1000 PUBLICATION TRENDS IN GASTROENTEROLOGY AND HEPATOLOGY OVER THE PAST 40 YEARS: AN ARTIFICIAL INTELLIGENCE ANALYSIS

2020 ◽  
Vol 158 (6) ◽  
pp. S-236-S-237 ◽  
Author(s):  
Gassan Kassim ◽  
Yiftach Barash ◽  
Eyal Klang ◽  
Ryan C. Ungaro ◽  
Jean Frederic Colombel
Keyword(s):  
Artificial Intelligence ◽  
Intelligence Analysis ◽  
Publication Trends ◽  
The Past

scholarly journals Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery

AI Magazine ◽  
2016 ◽  
Vol 37 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Hiroaki Kitano
Keyword(s):  
Artificial Intelligence ◽  
Nobel Prize ◽  
Scientific Discovery ◽  
Grand Challenge ◽  
Discovery Process ◽  
Biomedical Sciences ◽  
Cottage Industry ◽  
Left Behind ◽  
Highly Efficient ◽  
Cognitive Limitations

This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way. Such system may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence scientific discovery process.

scholarly journals Publication Trends by Indonesia and Malaysia Affiliated Researchers About Public Policy, Technology, and Economics

2020 ◽  
Vol 2 (2) ◽  
pp. 26
Author(s):  
Robertus In Nugroho Budisantoso ◽  
Sudi Mungkasi
Keyword(s):  
Public Policy ◽  
Research Articles ◽  
Publication Trends ◽  
Left Behind ◽  
Published Research

This paper aims to investigate the number of research articles authored by Indonesia and Malaysia affiliated researchers about three issues, namely, public policy, technology, and economics. We focus on articles indexed by Scopus in the ten year period 2010-2019. We obtain that Indonesia affiliated researchers are less productive from the beginning of the period. At the end of the period, Indonesia affiliated researchers produce more number of articles published internationally for public policy, technology, and economics issues. Furthermore, in total for any topics at the end of the period, Indonesia affiliated researchers publishes more articles. However, if we observe the number of published research articles with respect to the number of population, Indonesia’s is still left behind Malaysia’s.

scholarly journals MACHINE LEARNING IN THE FIELD OF MANUFACTURING

10.6036/10197 ◽  
2021 ◽  
Vol 96 (6) ◽  
pp. 600-604
Author(s):  
GAIZKA GOMEZ ESCUDERO ◽  
PABLO FERNANDEZ DE LUCIO ◽  
HAIZEA GONZALEZ BARRIO ◽  
AMAIA CALLEJA OCHOA ◽  
IZARO AYESTA REMENTERIA ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Community ◽  
Manufacturing Sector ◽  
Evolutionary Analysis ◽  
Artificial Intelligence Techniques ◽  
Left Behind ◽  
Current State

Artificial intelligence is already a pulsating reality in society, and all major companies are trying to apply its use to their products. In this context, the manufacturing sector could not be left behind. The industry has more and more alternatives to choose from when it comes to implementing artificial intelligence techniques. In this aspect, Machine Learning can be of great use when it comes to solving the challenges that arise in manufacturing. Therefore, this article presents a brief context of Machine Learning, followed by its current applications in the manufacturing sector together with a historical evolutionary analysis of the publications related to Machine Learning in the manufacturing sector. It ends with some conclusions regarding the current state of Machine Learning in the scientific community and in companies. Keywords: Machine Learning, Review, Manufacturing.

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