Lack of studies limit the integration of artificial intelligence into the clinic ArtificialIntelligence MachineLearning AI Coronavirus Disease Imaging COVID19 sahmriAU medrxivpreprint UniOulu UniversitySA OrionHealth Deakin
By Neha MathurMar 1 2023Reviewed by Benedette Cuffari, M.Sc. *Important notice: medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
Background Although some AI applications are in clinical trials to determine their potential integration into medical information systems, there remains a lack of studies demonstrating their ability to improve clinical results. However, studies have demonstrated the superiority of AI in experimental or pilot settings. Due to the reduced performance of these AI applications on external validation and low acceptance by clinicians, existing clinical workflows have yet to initiate their integration.
This systematic review used the Covidence software platform. While nine independent reviewers assessed scientific literature for translational value, the other two collected descriptive data from each study. A third reviewer compared evaluation scores and extracted data from all studies to resolve discrepancies if there were any.
Most studies performed remarkably in the capability component but did not score highly in the utility and service adoption components of the TEHAI framework. Surprisingly, studies with large datasets did not advance in domains of utility or adoption. This was similarly expected, as the number of studies for analysis would increase, differences in small versus large datasets would also become significant.
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