scholarly journals Application of artificial intelligence in clinical diagnosis and treatment: an overview of systematic reviews

Author(s):  
Shouyuan Wu ◽  
Jianjian Wang ◽  
Qiangqiang Guo ◽  
Hui Lan ◽  
Juanjuan Zhang ◽  
...  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jia Zhou ◽  
Meng Du ◽  
Shuai Chang ◽  
Zhiyi Chen

AbstractUltrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiography poses a challenge for sonographers to diagnose accurately. However, artificial intelligence for detection, functional evaluation, and disease diagnosis has gradually become an alternative for accurate diagnosis and treatment using echocardiography. This work discusses the current application of artificial intelligence in echocardiography technology, its limitations, and future development directions.


2020 ◽  
Author(s):  
Nicola Maffulli ◽  
Hugo C. Rodriguez ◽  
Ian W. Stone ◽  
Andrew Nam ◽  
Albert Song ◽  
...  

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.


2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Nicola Maffulli ◽  
Hugo C. Rodriguez ◽  
Ian W. Stone ◽  
Andrew Nam ◽  
Albert Song ◽  
...  

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.


2020 ◽  
Vol 26 (23) ◽  
pp. 2686-2691 ◽  
Author(s):  
Ioannis Doundoulakis ◽  
Christina Antza ◽  
Haralambos Karvounis ◽  
George Giannakoulas

Background: Anticoagulation in patients with pulmonary embolism. Objective: To identify how non-vitamin K antagonist oral anticoagulants are associated with multiple outcomes in patients with pulmonary embolism. Methods: We performed a systematic search of systematic reviews via multiple electronic databases from inception to August 19th, 2019, without language restriction. Two authors independently extracted data and assessed the methodological quality of the included systematic reviews using the ROBIS tool. Results: We found twelve systematic reviews. Eleven SRs collected their data from randomized clinical trials and one from observational studies. All the included studies were published between 2014 and 2019 in English. The methodological quality of the 12 systematic reviews was low to high. None of the systematic reviews, which are included in our overview of systematic reviews, has evaluated the overall quality of evidence outcome using the Grading of Recommendations Assessments, Development and Evaluation (GRADE) approach. Conclusion: This is the first effort to summarize evidence about non-vitamin K antagonist oral anticoagulants in an overview of systematic reviews focusing exclusively on patients with pulmonary embolism. The evidence suggests that the non-vitamin K antagonist oral anticoagulants seem to be more effective and safer than a dualdrug approach with LMWH- VKA.


2015 ◽  
Vol 10 (3) ◽  
pp. 204-212 ◽  
Author(s):  
Jenni Ilomaki ◽  
Natali Jokanovic ◽  
Edwin Tan ◽  
Eija Lonnroos

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