The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: an overview

Abstract

Abstract The aim of this overview article is to discuss the application of artificial intelligence (AI) tools in detecting and diagnosing malignant tumors based on different imaging modalities. The acronym PIRDs was used to create a search strategy. A comprehensive literature search was conducted on indexed databases and grey literature for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. The studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. The Super Vector Machine algorithm method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis. The use of AI tools benefitted less experienced radiologists more than experienced specialists on the use of machine learning and radiomic analysis in cancer identification. The combination of a CAD system, machine learning algorithms, and radiomic analysis seemed to be effective and promising in the identification and diagnosis of malignant tumors. However, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.

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