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Artificial intelligence in the judiciary: a systematic literature review on the practical applications

Stijn Van Ruymbeke et al.

Information and Communications Technology Law2026https://doi.org/10.1080/13600834.2026.2644818article
ABDC B
Weight
0.50

Abstract

Literature on the use of Artificial Intelligence (AI) in the judiciary is expanding rapidly. However, a comprehensive overview of which practical judicial applications stand to benefit from AI technologies remains absent. To address this gap, we conduct a systematic literature review (SLR) of 138 high-quality peer-reviewed journal articles identified through Scopus and Web of Science. In addition to a bibliometric analysis of temporal, geographical, and publication outlet trends, we apply a thematic and cascading synthesis approach to extract deeper insights. Our results show that research converges around two primary domains: (1) applications at the level of the internal management and organization of judiciaries, and (2) applications at the level of legal decision-making. These findings clarify the current landscape of practical AI applications in the judiciary. Additionally, we highlight some key challenges for judicial professionals and researchers seeking to initiate or assess AI implementation projects in judicial settings.

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https://doi.org/https://doi.org/10.1080/13600834.2026.2644818

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@article{stijn2026,
  title        = {{Artificial intelligence in the judiciary: a systematic literature review on the practical applications}},
  author       = {Stijn Van Ruymbeke et al.},
  journal      = {Information and Communications Technology Law},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/13600834.2026.2644818},
}

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Artificial intelligence in the judiciary: a systematic literature review on the practical applications

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.