scholarly journals Early Detection of Sepsis Using Artificial Intelligence: A Scoping Review Protocol

2020 ◽  
Author(s):  
Stefan Candefjord ◽  
Ivana Pepic ◽  
Robert Feldt ◽  
Lars Ljungström ◽  
Richard Torkar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Early Detection ◽  
Scoping Review ◽  
Web Search ◽  
Scientific Journal ◽  
Case Fatality ◽  
Cochrane Library ◽  
Suspected Sepsis ◽  
Wide Range

Abstract Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods: The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Databases/web search engines that will be used are PubMed, Web of Science, Scopus, IEEE Xplore, Google Scholar, Cochrane Library and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain. Ethics and dissemination: The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

scholarly journals Early detection of sepsis using artificial intelligence: a scoping review protocol

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Ivana Pepic ◽  
Robert Feldt ◽  
Lars Ljungström ◽  
Richard Torkar ◽  
Daniel Dalevi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Early Detection ◽  
Scoping Review ◽  
Web Search ◽  
Scientific Journal ◽  
Case Fatality ◽  
Cochrane Library ◽  
Suspected Sepsis ◽  
Wide Range

Abstract Background Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain. Ethics and dissemination The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

scholarly journals Early Detection of Sepsis Using Artificial Intelligence: A Scoping Review Protocol

2020 ◽  
Author(s):  
Ivana Pepic ◽  
Robert Feldt ◽  
Lars Ljungström ◽  
Richard Torkar ◽  
Daniel Dalevi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Early Detection ◽  
Scoping Review ◽  
Web Search ◽  
Scientific Journal ◽  
Case Fatality ◽  
Cochrane Library ◽  
Suspected Sepsis ◽  
Wide Range

Abstract Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods: The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 30 September 2020 will be taken into consideration and foreign language publications will not be considered, only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.Ethics and dissemination: The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

Artificial Intelligence and Ethics in Dentistry: A Scoping Review

2021 ◽  
pp. 002203452110138
Author(s):  
C.M. Mörch ◽  
S. Atsu ◽  
W. Cai ◽  
X. Li ◽  
S.A. Madathil ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Ethical Issues ◽  
Data Extraction ◽  
Cochrane Library ◽  
Data Sets ◽  
Ethical Challenges ◽  
Internal Validation ◽  
Neural Learning ◽  
Learning Techniques

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.

scholarly journals Application of Artificial Intelligence in the Prevention, Diagnosis and Treatment of Alzheimer’s Disease: New Hope for Dealing with Aging in China

2021 ◽  
Author(s):  
Hangtian Wang ◽  
Guofu Wang
Keyword(s):  
Artificial Intelligence ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Treatment Outcomes ◽  
Recent Progress ◽  
Diagnosis And Treatment ◽  
Cochrane Library ◽  
Poor Treatment ◽  
The Cochrane Library

Alzheimer’s disease (AD) has become a major issue around world, including China. The two major challenges for AD are the difficulty in early detection and poor treatment outcomes. Over the past decades, artificial intelligence (AI) was more and more widely used in the prevention, diagnosis and treatment of AD, which might be helpful to deal with the aging of population in China. Here, after a systematic literature searching on three English databases (MEDLINE, EMBASE, the Cochrane library), we briefly reviewed recent progress on the utilization of AI in the susceptibility analysis, diagnosis and management of AD. However, it is still in its infancy. More researches should be performed to improve the prognosis of patients with AD in the future.

scholarly journals Using social marketing for the promotion of cognitive health: a scoping review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e049947
Author(s):  
Mathilde Barbier ◽  
Caroline Schulte ◽  
Anna Kornadt ◽  
Carine Federspiel ◽  
Jean-Paul Steinmetz ◽  
...  
Keyword(s):  
Social Marketing ◽  
Scoping Review ◽  
Data Extraction ◽  
Scientific Journal ◽  
Grey Literature ◽  
Marketing Strategies ◽  
Cochrane Library ◽  
Cognitive Health ◽  
The Social ◽  
Scoping Reviews

IntroductionThe use of social marketing strategies to induce the promotion of cognitive health has received little attention in research. The objective of this scoping review is twofold: (i) to identify the social marketing strategies that have been used in recent years to initiate and maintain health-promoting behaviour; (ii) to advance research in this area to inform policy and practice on how to best make use of these strategies to promote cognitive health.Methods and analysisWe will use the five-stage methodological framework of Arksey and O’Malley. Articles in English published since 2010 will be searched in electronic databases (the Cochrane Library, DoPHER, the International Bibliography of the Social Sciences, PsycInfo, PubMed, ScienceDirect, Scopus). Quantitative and qualitative study designs as well as reviews will be considered. We will include those articles that report the design, implementation, outcomes and evaluation of programmes and interventions concerning social marketing and/or health promotion and/or promotion of cognitive health. Grey literature will not be searched. Two independent reviewers will assess in detail the abstracts and full text of selected citations against the inclusion criteria. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart for Scoping Reviews will be used to illustrate the process of article selection. We will use a data extraction form, present the results through narrative synthesis and discuss them in relation to the scoping review research questions.Ethics and disseminationEthics approval is not required for conducting this scoping review. The results of the review will be the first step to advance a conceptual framework, which contributes to the development of interventions targeting the promotion of cognitive health. The results will be published in a peer-reviewed scientific journal. They will also be disseminated to key stakeholders in the field of the promotion of cognitive health.

scholarly journals Personal identification with artificial intelligence under COVID-19 crisis: a scoping review

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Shinpei Matsuda ◽  
Hitoshi Yoshimura
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Literature Search ◽  
Personal Identification ◽  
Flow Diagram ◽  
Cochrane Library ◽  
Identification Methods ◽  
Institutional Infrastructure ◽  
The Face ◽  
Critical Consideration

Abstract Background Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical consideration during the coronavirus disease 2019 pandemic is that there is no contact between the subjects and personal identification systems. The aim of this study was to organize the recent 5-year development of contactless personal identification methods that use artificial intelligence. Methods This study used a scoping review approach to map the progression of contactless personal identification systems using artificial intelligence over the past 5 years. An electronic systematic literature search was conducted using the PubMed, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore databases. Studies published between January 2016 and December 2020 were included in the study. Results By performing an electronic literature search, 83 articles were extracted. Based on the PRISMA flow diagram, 8 eligible articles were included in this study. These eligible articles were divided based on the analysis targets as follows: (1) face and/or body, (2) eye, and (3) forearm and/or hand. Artificial intelligence, including convolutional neural networks, contributed to the progress of research on contactless personal identification methods. Conclusions This study clarified that contactless personal identification methods using artificial intelligence have progressed and that they have used information obtained from the face and/or body, eyes, and forearm and/or hand.

scholarly journals Usability and utility of eHealth for physical activity counselling in primary health care: a scoping review

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Apichai Wattanapisit ◽  
Titiporn Tuangratananon ◽  
Sanhapan Wattanapisit
Keyword(s):  
Physical Activity ◽  
Health Care ◽  
Primary Health Care ◽  
Scoping Review ◽  
Health Behaviours ◽  
Cochrane Library ◽  
Technical Problems ◽  
Primary Health ◽  
Wide Range ◽  
Full Text Review

Abstract Background Physical activity (PA) counselling is an effective approach to promote PA in primary health care (PHC). Barriers to PA counselling in PHC include time constraints, lack of knowledge and skills of providers, and systemic barriers. Using electronic health (eHealth) has the potential to promote PA. This scoping review aimed to identify usability and utility of eHealth for tailored PA counselling introduced in PHC settings. Methods A scoping review included primary research articles. The authors systematically searched six databases (Cochrane Library, CINAHL Complete, Embase, PubMed, Scopus and Web of Science) from the inception of the databases. The search terms consisted of three search components: intervention (PA counselling), platform (eHealth), and setting (PHC). Additional articles were included through reference lists. The inclusion criteria were research or original articles with any study designs in adult participants. Results Of 2501 articles after duplicate removal, 2471 articles were excluded based on the title and abstract screening and full text review. A total of 30 articles were included for synthesis. The eHealth tools had a wide range of counselling domains as a stand-alone PA domain and multiple health behaviours. The included articles presented mixed findings of usability and utility of eHealth for PA counselling among patients and providers in PHC settings. Technical problems and the complexity of the programmes were highlighted as barriers to usability. The majority of articles reported effective utility, however, several articles stated unfavourable outcomes. Conclusions eHealth has the potential to support PA counselling in PHC. Facilitators and barriers to eHealth usability should be considered and adapted to particular settings and contexts. The utility of eHealth for promoting PA among patients should be based on the pragmatic basis to optimise resources.

Health Equity in Artificial Intelligence and Primary Care Research: Protocol for a Scoping Review (Preprint)

2021 ◽  
Author(s):  
Jonathan Xin Wang ◽  
Sulaiman Somani ◽  
Jonathan H Chen ◽  
Sara Murray ◽  
Urmimala Sarkar
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Health Equity ◽  
Scoping Review ◽  
Underserved Populations ◽  
Care Delivery ◽  
Data Extraction ◽  
Cochrane Library ◽  
Preventative Care ◽  
The Impact

BACKGROUND Though artificial intelligence (AI) has potential to augment the patient-physician relationship in primary care, bias in intelligent healthcare systems has the potential to differentially impact vulnerable patient populations. OBJECTIVE The purpose of this scoping review is to summarize the extent to which AI systems in primary care examine the inherent bias towards or against vulnerable populations and appraise how these systems have mitigated the impact of such biases during their development. METHODS We will conduct a search update from an existing scoping review to identify AI and primary care articles in the following databases: Medline-OVID,Embase,CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, and arXiv. Two screeners will independently review all abstracts, titles and full-texts. The team will extract data using structured data extraction form and synthesize the results according to PRISMA-Scr guidelines. RESULTS This review will provide an assessment of the current state of healthcare equity within AI for primary care. Specifically, we will identify the degree to which vulnerable patients have been included, assess how bias is interpreted and documented, and understand the extent harmful biases are addressed. As of October 2020, the scoping review is in the title and abstract screening stage. The results are expected to be submitted for publication in fall of 2021. CONCLUSIONS AI applications in primary care are becoming an increasingly common tool in health care delivery, including in preventative care efforts for underserved populations. This scoping review aims to understand to what extent AI-primary care studies employ a health equity lens and take steps to mitigate bias.

scholarly journals Worden’s Task-Based Model for Treating Persistent Complex Bereavement Disorder During the Coronavirus Disease-19 Pandemic: A Narrative Review

2020 ◽  
Vol 8 (T1) ◽  
pp. 553-559
Author(s):  
Mohsen Khosravi
Keyword(s):  
High Speed ◽  
Case Fatality ◽  
Global Scale ◽  
Narrative Review ◽  
Cochrane Library ◽  
Anticipatory Grief ◽  
Wide Range ◽  
Considerable Impact ◽  
Persistent Complex Bereavement ◽  
Persistent Complex Bereavement Disorder

BACKGROUND: A wide range of studies has shown that the coronavirus disease (COVID)-2019 pandemic could cause many deaths on the global scale by the end of 2020 because of the high speed of transmission and predicted case-fatality rates. AIM: This paper is a narrative review aiming to address the treatment of persistent complex bereavement disorder (PCBD) during the COVID-19 crisis using Worden’s task-based model. MATERIALS AND METHODS: Related papers published from 2000 to 2020 were searched in the EMBASE, PubMed, Web of Science, Scopus, Cochrane Library, and Google Scholar databases. Bereavement, COVID-19, pandemics, and Worden’s task-based model constituted the search terms. A narrative technique was implemented (including reading, writing, thinking, interpreting, arguing, and justifying) for material synthesis and creating a compelling and cohesive story. RESULTS: A few studies have specifically addressed the grief experiences within the COVID-19 crisis. They managed to identify some potential obstacles to grieving during the pandemic, namely, “anticipatory grief” and “multiple losses.” This study tried to use Worden’s task-based model to address the treatment of PCBD during the pandemic. CONCLUSIONS: Despite the paucity of information, Worden’s task-based model seems to have a considerable impact on the reduction of the PCBD symptoms. Nonetheless, further research is needed to perceive the effect of this approach on PCBD during the COVID-19 pandemic.

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