Explainable artificial intelligence in industrial machine vision: A bibliometric analysis
Main Article Content
Abstract
This study maps the structural properties of the explainable artificial intelligence (XAI) in industrial machine vision research field at the corpus level. Explainability requirements for industrial machine vision systems have intensified since the European Union Artificial Intelligence Act classified high-risk image-based inspection systems as requiring traceable automated decisions, yet the resulting research field had not been systematically mapped. 526 peer-reviewed publications retrieved from Scopus and Web of Science for the period 2015 to March 2026 were analysed using six sequential bibliometric analyses: publication trend, source productivity, geographic distribution, keyword co-occurrence network, thematic mapping, and thematic evolution. All analyses were conducted using the bibliometrix package (version 4.3.0) in R (version 4.4.1). Annual output grew from 6 papers in 2017 to 158 in 2025 (compound annual growth rate: approximately 50%), with a 94% single-year acceleration in 2022–2023 coinciding with early EU AI Act commentary and the displacement of convolutional architectures by vision transformers. Among XAI method terms, Grad-CAM leads at 40 keyword occurrences, more than three times LIME (11) and nearly six times SHAP (7). China and India together account for 30.0% of the corpus (158 papers). Thematic mapping shows deep learning migrating from a motor to a basic theme between the sub-periods 2015–2021 and 2022–2026, while decision support systems and ethical artificial intelligence emerge as new motor themes. This study presents the first dual-database bibliometric analysis of XAI in industrial machine vision, providing a reference map for researchers entering the field, journal editors tracking disciplinary boundaries, and policymakers benchmarking research response to regulatory pressure. The findings indicate that the field has moved from performance demonstration toward regulatory-compliance integration, with post-hoc visual explanation as the dominant methodological choice.
Keywords
bibliometric analysis; explainable artificial intelligence; industrial machine vision; Industry 4.0
Article Details
Field of Economic (JEL Codes)
C55 - Large Data Sets: Modeling and Analysis - Econometric Modeling, L60 - General - Industry Studies: Manufacturing, O33 - Technological Change: Choices and Consequences • Diffusion Processes - Innovation • Research and Development • Technological Change • Intellectual Property Rights
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