scholarly journals Sustainable Production in Cement via Artificial Intelligence based Decision Support System: Case Study

2021 ◽  
pp. e00628
Author(s):  
Kübra Tümay Ateş ◽  
Cenk Şahin ◽  
Yusuf Kuvvetli ◽  
Bülent A. Küren ◽  
Aykut Uysal
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Sustainable Production

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

An artificial intelligence-based clinical decision support system for large kidney stone treatment

2019 ◽  
Vol 42 (3) ◽  
pp. 771-779 ◽  
Author(s):  
Tayyebe Shabaniyan ◽  
Hossein Parsaei ◽  
Alireza Aminsharifi ◽  
Mohammad Mehdi Movahedi ◽  
Amin Torabi Jahromi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Kidney Stone ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Stone Treatment

Decision Support System for Radiotherapy Patient Scheduling: Thai Cancer Center Case Study

2021 ◽  
Author(s):  
Chawis Boonmee ◽  
Nirand Pisutha-Arnond ◽  
Wichai Chattinnawat ◽  
Pooriwat Muangwong ◽  
Wannapha Nobnop ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cancer Center ◽  
Patient Scheduling

scholarly journals Design of Condition-Based Decision Support System for Preventive Maintenance

2017 ◽  
Vol 20 (1) ◽  
pp. 19-22
Author(s):  
Róbert Galamboš ◽  
Jana Galambošová ◽  
Vladimír Rataj ◽  
Miroslav Kavka
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Preventive Maintenance ◽  
Maintenance Costs

Abstract Presented paper deals with the topic of preventive maintenance. A decision support system was designed, incorporating historical as well as forecast information to calculate the time remaining to preventive maintenance. The designed system optimizes maintenance costs without any further investment and running costs. An algorithm of the designed system is introduced and a case study of its implementation is described in the paper.

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