PNS101 Evaluation of Artificial Intelligence within DistillerSR Software As a Second Reviewer for a Systematic Literature Review

2021 ◽  
Vol 24 ◽  
pp. S191
Author(s):  
A. Patel ◽  
C. Hallock ◽  
N. Fusco ◽  
S.M. Cadarette ◽  
L. Mody
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Systematic Literature Review

The Use of Artificial Intelligence in Diagnostic Medical Imaging: Systematic Literature Review

2021 ◽  
Author(s):  
Lamija Hafizovic ◽  
Aldijana Causevic ◽  
Amar Deumic ◽  
Lemana Spahic Becirovic ◽  
Lejla Gurbeta Pokvic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Literature Review ◽  
Systematic Literature Review

scholarly journals PNS319 USE OF ARTIFICIAL INTELLIGENCE WITH DISTILLERSR SOFTWARE AS A REVIEWER FOR A SYSTEMATIC LITERATURE REVIEW OF COST-EFFECTIVENESS MODELS

2019 ◽  
Vol 22 ◽  
pp. S818
Author(s):  
B. Smela-Lipińska ◽  
V. Taieb ◽  
I. Myjak ◽  
J. Tetzlaff ◽  
P. O'Blenis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cost Effectiveness ◽  
Literature Review ◽  
Systematic Literature Review

Debunking the myth of industry 4.0 in health care: insights from a systematic literature review

2020 ◽  
Vol 32 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Mauro Cavallone ◽  
Rocco Palumbo
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Health Services ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Industry 4.0 ◽  
Services Quality ◽  
State Of The Art ◽  
The State ◽  
Content Type

PurposeIndustry 4.0, artificial intelligence and digitalization have got a momentum in health care. However, scholars and practitioners do not agree on their implications on health services' quality and effectiveness. The article aims at shedding light on the applications, aftermaths and drawbacks of industry 4.0 in health care, summarizing the state of the art.Design/methodology/approachA systematic literature review was undertaken. We arranged an ad hoc research design, which was tailored to the study purposes. Three citation databases were queried. We collected 1,194 scientific papers which were carefully considered for inclusion in this systematic literature review. After three rounds of analysis, 40 papers were taken into consideration.FindingsIndustry 4.0, artificial intelligence and digitalization are revolutionizing the design and the delivery of care. They are expected to enhance health services' quality and effectiveness, paving the way for more direct patient–provider relationships. In addition, they have been argued to allow a more appropriate use of available resources. There is a dark side of health care 4.0 involving both management and ethical issues.Research limitations/implicationsIndustry 4.0 in health care should not be conceived as a self-nourishing innovation; rather, it needs to be carefully steered at both the policy and management levels. On the one hand, comprehensive governance models are required to realize the full potential of health 4.0. On the other hand, the drawbacks of industry 4.0 should be timely recognized and thoroughly addressed.Originality/valueThe article contextualizes the state of the art of industry 4.0 in the health care context, providing some insights for further conceptual and empirical developments.

scholarly journals Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

2020 ◽  
Vol 7 (2) ◽  
pp. 129-134
Author(s):  
Takudzwa Fadziso
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Modern Times ◽  
Predictive Algorithms ◽  
Asthma Patients

In modern times, the collection of data is not a big deal but using it in a meaningful is a challenging task. Different organizations are using artificial intelligence and machine learning for collecting and utilizing the data. These should also be used in the medical because different disease requires the prediction. One of these diseases is asthma that is continuously increasing and affecting more and more people. The major issue is that it is difficult to diagnose in children. Machine learning algorithms can help in diagnosing it early so that the doctors can start the treatment early. Machine learning algorithms can perform this prediction so this study will be helpful for both the doctors and patients. There are different machine learning predictive algorithms are available that have been used for this purpose.  

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