Enhancing diagnostic accuracy using artificial intelligence-powered imaging, lab analysis, and real-time monitoring tools

Abstract

In vitro diagnostics play a critical role in effective early disease detection and in advancing the diagnosis and treatment response. Smart diagnostics is defined as the in vitro diagnostic application of artificial intelligence (AI) and machine learning technology through algorithm development and integration within the domain of IVD to augment its utility. Despite reducing its predictive performance, a significant proportion of the literature suggested that AI-powered algorithms had generally helped improve diagnostic accuracy in studies using imaging, laboratory analysis, or real-time monitoring tools. This paper presents a systematic review of a range of smart diagnostics and assesses the net effect on diagnostics accuracy in the context of method and reporting quality of the literature.

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