Future implications of artificial intelligence in lung cancer screening: a systematic review.

Abstract

Objectives: The aim of this study was to systematically review the literature to assess the application of AI-based interventions in lung cancer screening, and its future implications.

Methods: Relevant published literature was screened using PRISMA guidelines across three databases: PubMed, Scopus, and Web of Science. Search terms for article selection included "artificial intelligence," "radiology," "lung cancer," "screening," and "diagnostic." Included studies evaluated the use of AI in lung cancer screening and diagnosis.

Results: Twelve studies met the inclusion criteria. All studies concerned the role of AI in lung cancer screening and diagnosis. The AIs demonstrated promising ability across four domains: (1) detection, (2) characterization and differentiation, (3) augmentation of the work of human radiologists, (4) AI implementation of the LUNG-RADS framework and its ability to augment this framework. All studies reported positive results, demonstrating in some cases AI's ability to perform these tasks to a level close to that of human radiologists.

Conclusions: The AI systems included in this review were found to be effective screening tools for lung cancer. These findings hold important implications for the future use of AI in lung cancer screening programmes as they may see use as an adjunctive tool for lung cancer screening that would aid in making early and accurate diagnosis.

Advances in knowledge: AI-based systems appear to be powerful tools that can assist radiologists with lung cancer screening and diagnosis.