Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 13, 2023
Date Accepted: Nov 11, 2024
Artificial intelligence performance in image-based cancer identification: an overview of the systematic reviews
ABSTRACT
Background:
Artificial intelligence (AI) have gained popularity in facilitating cancer diagnosis and treatment planning.
Objective:
Here, we performed an umbrella review to summarize and critically evaluate the evidence for AI-based imaging diagnosis of cancer.
Methods:
PubMed, Embase, Web of science, Cochrane, and IEEE databases were searched for relevant systematic reviews or meta-analyses from inception to 27 September 2022. Two independent investigators abstracted data and assessed the quality of evidence using Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. We further assessed the quality of evidence of per meta-analysis by applying the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) criteria (PROSPERO CRD42022364278).
Results:
PubMed, Embase, Web of science, Cochrane, and IEEE databases were searched for relevant systematic reviews or meta-analyses from inception to 27 September 2022. Two independent investigators abstracted data and assessed the quality of evidence using Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. We further assessed the quality of evidence of per meta-analysis by applying the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) criteria (PROSPERO CRD42022364278).
Conclusions:
Although AI shows a great potential to lead to accelerated, accurate, and more objective diagnoses of multiple cancers, there are still hurdles to overcome before the implementation in clinical setting. The present findings do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer identification. Clinical Trial: PROSPERO CRD42022364278
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.