Aspects of the use of artificial intelligence for disease screening and diagnosis: a narrative review

Abstract

Background. The active implementation of artificial intelligence (AI) in healthcare significantly improves the effectiveness of early diagnosis of various diseases. Promising areas for AI implementation include diagnostic methods based on voice analysis, remote blood flow monitoring using photoplethysmography, eye movement tracking, and the use of smart devices for continuous health monitoring. A key objective is the development of integrated systems for comprehensive medical screening. Aim. To evaluate the literature and analyze the results of artificial intelligence implementation for the early detection of diseases. Materials and methods. A search was conducted in the databases PubMed (Medline), Google Scholar, eLibrary, Web of Science, Scopus, CyberLeninka for English- and Russian-language publications. We used the keywords "screening," "diagnostics," "artificial intelligence," "machine learning," "disease," "screening," "diagnostics," "artificial intelligence," "disease," "machine learning," and "deep learning." Studies from 2015 to 2025 were included based on independent review by three researchers who reached a consensus. Results. Thirty-one papers from 1,141 publications that met the search criteria were included for this review. Conclusions. AI-based disease screening and diagnosis provides valuable information about a patient's condition, reducing the risk of human error and missing early signs of disease.

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