What databases should we include in a comprehensive search strategy protocol for systematic reviews of artificial intelligence? (Preprint)

2020 ◽  
Author(s):  
Dylan Mordaunt
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Systematic Reviews ◽  
Search Strategy ◽  
Safety Outcomes ◽  
Comprehensive Search ◽  
Comprehensive Search Strategy

UNSTRUCTURED This is a commentary of the article by Choudhury and Asan entitled, “Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review”.

scholarly journals Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury ◽  
Onur Asan
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Literature Review ◽  
Decision Tree ◽  
Systematic Literature Review ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Quantitative Studies ◽  
Safety Outcomes ◽  
Tree Models

BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

scholarly journals Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

10.2196/18599 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18599 ◽  
Author(s):  
Avishek Choudhury ◽  
Onur Asan
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Literature Review ◽  
Decision Tree ◽  
Systematic Literature Review ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Quantitative Studies ◽  
Safety Outcomes ◽  
Tree Models

Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

scholarly journals Developing a Comprehensive Search Strategy for Evidence Based Systematic Reviews

2008 ◽  
Vol 3 (1) ◽  
pp. 3 ◽  
Author(s):  
Julia B. DeLuca ◽  
Mary M. Mullins ◽  
Cynthia M. Lyles ◽  
Nicole Crepaz ◽  
Linda Kay ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Search Strategy ◽  
Evidence Based Practice ◽  
Reference List ◽  
Evidence Based ◽  
Manual Search ◽  
Comprehensive Search ◽  
Comprehensive Search Strategy ◽  
Automated Search ◽  
Hiv Aids

Objective: As the health care field moves towards evidence-based practice, it becomes ever more critical to conduct systematic reviews of research literature for guiding programmatic activities, policy-making decisions, and future research. Conducting systematic reviews requires a comprehensive search of behavioral, social, and policy research to identify relevant literature. As a result, the validity of the systematic review findings and recommendations is partly a function of the quality of the systematic search of the literature. Therefore, a carefully thought out and organized plan for developing and testing a comprehensive search strategy should be followed. Methods: The comprehensive search strategies, including automated and manual search techniques, were developed, tested, and implemented to locate published and unpublished citations to build a database of HIV/AIDS and STD literature for the CDC’s HIV Prevention Research Synthesis Project. The search incorporates various automated and manual search methods to decrease the chance of missing pertinent information. The automated search was implemented in MEDLINE, EMBASE, PsycINFO, Sociological Abstracts and AIDSLINE some of the key databases for biomedical, psychological, behavioral science, and public health literature. These searches utilized indexing, keywords including truncation, proximity, and phrases. The manual search method includes physically examining journals (hand searching), reference list checks, and researching key authors. Results: Using automated and manual search components, the PRS search strategy retrieved 17,493 HIV/AIDS/STD prevention focused articles for the years 1988-2005. The automated search found 91% and the manual search contributed 9% of the articles reporting on HIV/AIDS or STD interventions with behavior/biologic outcomes. Among the automated search citations, 48% were found in one database only (20% MEDLINE, 18% PsycINFO, 8 % EMBASE, 2% Sociological Abstracts). Conclusions: A comprehensive base of literature requires searching multiple databases and methods of manual searching in order to locate all relevant citations. Understanding the project needs, the limitations of different electronic databases, and other methods for developing and refining a search are vital in planning an effective and comprehensive search strategy. Reporting standards for literature searches as part of the broader push for procedurally transparent and reproducible systematic reviews is not only advisable, but good evidence-based practice.

Artificial intelligence and big data in tourism: a systematic literature review

2020 ◽  
Vol 11 (2) ◽  
pp. 343-367 ◽  
Author(s):  
Dimitra Samara ◽  
Ioannis Magnisalis ◽  
Vassilios Peristeras
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
New Technologies ◽  
Recent Literature ◽  
Content Type ◽  
Analysis Methodology ◽  
Tourism Sector

Purpose This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent literature. Design/methodology/approach A systematic literature review was conducted under the McKinsey’s Global Institute (Talwar and Koury, 2017) methodological perspective that identifies the four ways (i.e. project, produce, promote and provide) in which BDAI creates value. The authors enhanced this analysis methodology by depicting relevant challenges as well. Findings The findings imply that BDAI create value for the tourism sector through appropriately identified disseminations. The benefits of adopting BDAI strategies include increased efficiency, productivity and profitability for tourism suppliers combined with an extremely rich and personalized experience for travellers. The authors conclude that challenges can be bypassed by adopting a BDAI strategy. Such an adoption will stand critical for the competitiveness and resilience of existing established and new players in the tourism sector. Originality/value Besides identifying the benefits that BDAI brings in the tourism sector, the research proposes a guidebook to overcome challenges when introducing such new technologies. The exploration of the BDAI literature brings important implication for managers, academicians and consumers. This is the first systematic review in an area and contributes to the broader e-commerce marketing, retailing and e-tourism research.

scholarly journals A Scholarly Review of the Safety and Efficacy of Intranasal Corticosteroids Preparations in the Treatment of Chronic Rhinosinusitis

2020 ◽  
pp. 014556132096772
Author(s):  
Luis Macias-Valle ◽  
Alkis J. Psaltis
Keyword(s):  
Chronic Rhinosinusitis ◽  
Search Strategy ◽  
Intranasal Corticosteroids ◽  
Quality Of Life Measures ◽  
General Quality ◽  
Comprehensive Search ◽  
High Level ◽  
Comprehensive Search Strategy

Objective: The purpose of this scholarly review is to present an update of the efficacy, safety, and distribution of intranasal corticosteroids (INCS) in the context of treatment for chronic rhinosinusitis (CRS). Materials and Methods: A literature review from 1999 to 2020 of MEDLINE, PubMed, and EMBASE databases was performed, using a comprehensive search strategy. Studies reporting on efficacy, safety, and distribution of all INCS formulations, both Food and Drug Administration (FDA) and non-FDA approved, were reviewed. Results and Conclusions: High-level evidence publications and position papers support the role of INCS in medical treatment for CRS. Significant improvement in disease-specific and general quality of life measures is observed with all formulations of INCS. Overall, the use of both FDA and published non-FDA INCS appears to be safe. Several novel distribution devices might improve penetration to specific areas within the sinuses.

scholarly journals Role of Artificial Intelligence in Circular Manufacturing: A Systematic Literature Review

2021 ◽  
Vol 54 (1) ◽  
pp. 367-372
Author(s):  
Federica Acerbi ◽  
Dai Andrew Forterre ◽  
Marco Taisch
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Systematic Literature Review

Systematic Literature Review of Role of Applied Geomatics in Mapping and Tracking Corona Virus

2020 ◽  
Vol 12 (8) ◽  
Author(s):  
Nazirah Mohamad Abdullah ◽  
◽  
Shuib Rambat ◽  
Mohammad Hafiz Mohd Yatim ◽  
Abdullah Hisam Omar ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Corona Virus

scholarly journals Pain Relief and Safety Outcomes with Cervical 10 kHz Spinal Cord Stimulation: Systematic Literature Review and Meta-analysis

2021 ◽  
Author(s):  
Ganesan Baranidharan ◽  
Beatrice Bretherton ◽  
Craig Montgomery ◽  
John Titterington ◽  
Tracey Crowther ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Pain Relief ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Spinal Cord Stimulation ◽  
Meta Analysis ◽  
Safety Outcomes
Sign in / Sign up

Export Citation Format

Share Document