Artificial Intelligence for Infant Pain Detection: A Bibliometric and Knowledge Mapping Review

Artificial Intelligence (AI) has been created to be a potent tool that can help clinical decision-making by offering objective and ongoing monitoring of neonatal health. Unlike adults, infants are unable to communicate pain verbally and conventional pain measurement relies primarily on subjective and observational pain scales. This dependence introduces the possibility of some interrater variability and observer bias that may result in variability in assessment and suboptimal pain management in neonatal intensive care settings. To address these challenges, this study aims to provide a complete overview of the literature on AI-based infant pain detection and draw an intellectual map of this field by conducting a comprehensive bibliometric and science-mapping review. The systematic search was carried out by employing the Scopus database with an advanced and structured keyword search query to retrieve relevant English-language journal articles from 2010 to 2026. After screening with predefined inclusion criteria, a final set of 153 core documents was retained for analysis. The research productivity and citation impacts of the identified articles were assessed, and emerging thematic structures were identified through bibliometric processing and network visualization using a web-based bibliometric analysis platform, BiblioSpy^®. Quantitative results demonstrate that the field is dynamic and expanding rapidly, with an annual publication growth rate of 29.06% and 2474 cumulative citations. The h-index of the analyzed corpus is 26, and the g-index is 46, with an average of 6.08 authors per publication and an 88.24% international co-authorship rate, suggesting a very collaborative environment. To conclude, quantitative keyword temporal tracking confirms this evolution: traditional binary algorithms display earlier average publication timelines (2019.0), whereas deep learning, Convolutional Neural Networks (CNNs), and multi-signal predictive models dominate recent outputs (2023.8-2024.5), demonstrating a quantitative shift from isolated binary classifiers toward continuous, explainable, and clinically viable multimodal monitoring systems. The results indicate that although the initial studies have been done on only binary classification of facial expressions and/or cry acoustics, the current research is moving towards continuous, explainable, and clinically viable multimodal monitoring systems.