scholarly journals Artificial Intelligence in clinical care amidst COVID-19 Pandemic: A Systematic Review

2021 ◽  
Author(s):  
Eleni S. Adamidi ◽  
Konstantinos Mitsis ◽  
Konstantina S. Nikita
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Clinical Care

scholarly journals Artificial Intelligence and Machine Learning in Orthopedic Surgery: A Systematic Review Protocol

2020 ◽  
Author(s):  
Nicola Maffulli ◽  
Hugo C. Rodriguez ◽  
Ian W. Stone ◽  
Andrew Nam ◽  
Albert Song ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Clinical Diagnosis ◽  
Systematic Reviews ◽  
Orthopedic Surgery ◽  
Case Reports ◽  
Clinical Care ◽  
Primary Objective ◽  
Operative Outcomes

Abstract Background: Artificial Intelligence (AI) and Machine Learning (ML) is interwoven into our everyday lives and has grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery.Methods: A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions, acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies, do not relate to orthopedic interventions, or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and results of meta-analyses will not be included in the analysis.Discussion: Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies, and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed.Trial registration: The Protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.

scholarly journals Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Nicola Maffulli ◽  
Hugo C. Rodriguez ◽  
Ian W. Stone ◽  
Andrew Nam ◽  
Albert Song ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Clinical Diagnosis ◽  
Systematic Reviews ◽  
Orthopedic Surgery ◽  
Case Reports ◽  
Clinical Care ◽  
Primary Objective ◽  
Operative Outcomes

Abstract Background Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery. Methods A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis. Discussion Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed. Trial registration The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.

scholarly journals Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review

2021 ◽  
Vol 11 (7) ◽  
pp. 3253
Author(s):  
Umile Giuseppe Longo ◽  
Sergio De Salvatore ◽  
Vincenzo Candela ◽  
Giuliano Zollo ◽  
Giovanni Calabrese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Orthopaedic Surgery ◽  
Resident Training ◽  
Comprehensive Search ◽  
Virtual And Augmented Reality ◽  
Orthopaedic Training ◽  
Meta Analyses

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

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.

Sign in / Sign up

Export Citation Format

Share Document