SynthesisPloS one2025
Prioritizing challenges in AI adoption for the legal domain: A systematic review and expert-driven AHP analysis.
Synthesis in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence based on forensic medical databases: construction, application, and prospects.Forensic science, medicine, and pathology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research explores the crucial challenges influencing the adoption of Artificial Intelligence (AI) in the legal domain, a field facing escalating challenges due to rapid technological advancements. We have comprehensively identified, extracted, and evaluated 11 pivotal factors across legal, technical, and socio-ethical dimensions through a systematic review based on the PRISMA guideline. These factors are categorized into three principal groups. Utilizing an analytic hierarchy process (AHP), our innovative approach assesses the relative importance of these challenges based on data meticulously gathered from eight domain experts in law and AI. Our findings pinpoint legal aspects as the paramount category, with liability as the foremost concern among the analyzed factors. These insights offer robust and actionable guidelines for integrating AI into legal practices and underscore this study's unique contribution to bridging the gap between legal professionals and technology developers. By highlighting the practical applications of our results, this paper facilitates a deeper understanding and proactive engagement with the essential considerations pivotal for the future adoption and evolution of AI within the legal domain.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.