scholarly journals Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review (Preprint)

2020 ◽  
Author(s):  
A Hasan Sapci ◽  
H Aylin Sapci
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Informatics ◽  
Data Analytics ◽  
Learning Experience ◽  
Meta Analysis ◽  
Improve Patient Care ◽  
Time Data ◽  
Current State ◽  
Intelligence Education

BACKGROUND The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. OBJECTIVE The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. METHODS A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. RESULTS This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. CONCLUSIONS To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

scholarly journals Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

10.2196/19285 ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e19285 ◽  
Author(s):  
A Hasan Sapci ◽  
H Aylin Sapci
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Informatics ◽  
Data Analytics ◽  
Learning Experience ◽  
Meta Analysis ◽  
Improve Patient Care ◽  
Time Data ◽  
Current State ◽  
Intelligence Education

Background The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. Objective The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. Methods A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. Results This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. Conclusions To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

scholarly journals Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043665
Author(s):  
Srinivasa Rao Kundeti ◽  
Manikanda Krishnan Vaidyanathan ◽  
Bharath Shivashankar ◽  
Sankar Prasad Gorthi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Ischaemic Stroke ◽  
Study Protocol ◽  
Systematic Reviews ◽  
Acute Ischaemic Stroke ◽  
Meta Analysis ◽  
Large Vessel ◽  
Sufficient Data

IntroductionThe use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).Methods and analysisWe will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration numberCRD42020179652.

scholarly journals Work Engagement – A Systematic Review of Polish Research

2017 ◽  
Vol 48 (2) ◽  
pp. 175-187
Author(s):  
Anita Pollak ◽  
Małgorzata Chrupała-Pniak ◽  
Patrycja Rudnicka ◽  
Mateusz Paliga
Keyword(s):  
Systematic Review ◽  
Work Engagement ◽  
Meta Analysis ◽  
Organizational Psychology ◽  
Measurement Methods ◽  
The Past ◽  
Current State ◽  
State Of Research ◽  
Meta Analyses ◽  
Positive Work

Abstract Over the past decade work engagement has gained both business and academia attention. With growing number of studies and meta-analyses the concept of work engagement is one of the pillars of positive work and organizational psychology. This systematic review presents the current state of research on work engagement in Poland. Results confirmed that work-engagement studies have not yet reached the threshold to conduct meta-analysis. The review of measurement methods and synthesis of findings allows to identify strengths and gaps in Polish studies. Discussion of limitations and biases in current research is accompanied with urge to overcome them and develop thriving stream of research on work engagement.

scholarly journals Teaching Lifelong Research Skills in Residency: Implementation and Outcome of a Systematic Review and Meta-Analysis Course

2015 ◽  
Vol 7 (3) ◽  
pp. 445-450 ◽  
Author(s):  
Seth Himelhoch ◽  
Sarah Edwards ◽  
Mark Ehrenreich ◽  
M. Philip Luber
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Scientific Literacy ◽  
Learning Experience ◽  
Meta Analysis ◽  
Academic Productivity ◽  
Fisher Exact Test ◽  
Psychiatry Residents ◽  
Research Skills ◽  
Meta Analyses

ABSTRACT Background There is rising concern that fundamental scientific principles critical to lifelong learning and scientific literacy are not sufficiently addressed during residency. Objective We describe the development, implementation, and evaluation of a systematic review and meta-analysis course designed to improve residents' research literacy. Intervention We developed and implemented a novel, interactive, web-enhanced course for third-year psychiatry residents to provide the theoretical and methodological tools for conducting and reporting systematic reviews and meta-analyses. The course is based on Bloom's learning model, and established criteria for reporting systematic reviews and meta-analyses. Eight sequential learning objectives were linked to 8 well-specified assignments, with the objectives designed to build on one another and lead to the creation of a scientific manuscript. Results From 2010–2014, 54 third-year psychiatry residents (19 unique groups) successfully completed the course as part of a graduation requirement. The majority rated the course as being good or very good, and participants reported a statistically significant increase in their confidence to conduct systematic reviews (χ2 = 23.3, P < .05) and meta-analyses (Fisher exact test, P < .05). Estimated total dedicated resident and faculty time over a period of 36 weeks was 36 to 72 hours and 60 hours, respectively. Residents' academic productivity included 11 conference presentations and 4 peer-reviewed published manuscripts, with 2 residents who were awarded honors for their projects. Conclusions A formal training course in systematic reviews and meta-analyses offers a valuable learning experience, which enhances residents' research skills and academic productivity in a feasible and sustainable approach.

scholarly journals The association between occupational loading and spine degeneration on imaging – a systematic review and meta-analysis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Luciana G. Macedo ◽  
Michele C. Battié
Keyword(s):  
Systematic Review ◽  
Disc Degeneration ◽  
Signal Intensity ◽  
Structural Damage ◽  
Meta Analysis ◽  
Cross Sectional ◽  
Current State ◽  
Disc Bulge ◽  
Spine Degeneration ◽  
Relationship Of

Abstract Background There are inconsistencies in findings regarding the relationship of occupational loading with spinal degeneration or structural damage. Thus, a systematic review was conducted to determine the current state of knowledge on the association of occupational loading and spine degeneration on imaging. Methods We performed electronic searches on MEDLINE, CINAHL and EMBASE. We included cross-sectional, case control and cohort studies evaluating occupational loading as the exposure and lumbar spine structural findings on imaging as the outcomes. When possible, results were pooled. Results Seventeen studies were included in the review. Ten studies evaluated the association of occupational loading with disc degeneration (signal intensity), four of which were pooled into a meta-analysis. Of the 10 studies, only two did not identify a relationship between occupation loading and disc degeneration. A meta-analysis including four of the studies demonstrated an association between higher loading and degeneration for all spinal levels, with odds ratios between 1.6 and 3.3. Seven studies evaluated disc height narrowing and seven evaluate disc bulge, with six and five identifying an association of loading and with imaging findings respectively. Three studies evaluated modic changes and one identified and association with occupational load. Conclusions There was moderate evidence suggesting a modest association between occupational loading and disc degeneration (signal intensity), and low-quality evidence of an association between occupational loading and disc narrowing and bulging.

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