The Evolution of Legal AI Applications: A Scientometric Analysis
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Abstract
This study employs a scientometric approach to examine 421 Legal AI publications indexed in the Scopus database between 2015 and 2025, mapping the structural evolution, key thematic clusters, and emerging research frontiers of the field. The empirical findings reveal a gradual increase in publication volume after 2019, followed by an unprecedented expansion after 2022. While core tasks such as legal decision support and legal information retrieval constitute the largest topic categories, recent scholarship demonstrates a clear shift toward generative AI, legal chatbots, and critical questions regarding ethics, privacy, and algorithmic accountability. Longitudinal keyword analysis confirms a transition from general machine learning models toward natural language processing, large language models, explainability, and responsible AI governance. Furthermore, geographic analysis shows that despite increasing global participation, output remains concentrated in a limited group of countries led by the United States, China, and the United Kingdom. The paper concludes that future Legal AI research must move beyond isolated task accuracy to evaluate system validity, source reliability, human oversight, and institutional adaptation within complex legal frameworks.
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