The review organizes the sprawling landscape of legal AI into five distinct application domains. The first is AI-based online dispute resolution, systems designed to mediate and arbitrate conflicts without physical courtrooms, a domain that expanded dramatically as courts worldwide sought remote alternatives. The second covers legal information and knowledge retrieval, the search engines of the legal world that help lawyers and judges locate relevant statutes, precedents, and scholarly commentary from mountains of text. The third domain encompasses document analysis and text mining, where algorithms review contracts, evidence files, and case records at speeds no human team could match. The fourth includes expert and consultation systems that provide preliminary legal guidance to citizens who cannot afford counsel, while the fifth and most controversial domain involves judgment and outcome prediction, models that attempt to forecast how courts will rule on specific disputes.

Underneath these applications lies a profound architectural shift that the authors document in detail. Early legal AI systems were built on rule-based approaches, essentially vast hand-crafted decision trees encoding legal logic as explicit if-then statements. These systems were transparent but brittle, unable to cope with the ambiguity and linguistic variation that define real legal practice. The review highlights a clear transition through machine learning to transformer-based architectures and, most recently, large language models. Transformers, the neural network design that underpins models like GPT and its competitors, excel at capturing long-range dependencies in text, which makes them naturally suited to legal documents that routinely run to hundreds of pages with cross-references scattered throughout.

This architectural evolution matters because legal language is arguably one of the hardest domains in natural language processing. Legal texts combine archaic terminology, jurisdiction-specific conventions, deliberately precise definitions that differ from everyday usage, and reasoning structures that blend factual finding with normative judgment. A model that performs brilliantly on general news text may stumble badly when asked to distinguish between dicta and binding precedent, or to recognize that a single statutory word carries decades of interpretive case law. The datasets used to train and evaluate these systems therefore become critical, and the review synthesizes the dominant datasets and evaluation practices across the field, revealing a community still wrestling with how to measure whether a legal AI system actually works.

The challenges the reviewers identify form a sobering catalogue. Data scarcity sits near the top: high-quality, labeled legal data is expensive to produce, often confidential, and unevenly distributed across jurisdictions and languages. Many published models are trained on narrow datasets from a single country or court system, and the review finds limited generalizability as a persistent weakness, meaning a judgment-prediction model trained on one nation’s case law may fail catastrophically when applied elsewhere, or even to a different court within the same nation. This matters enormously because legal systems are deliberately jurisdiction-specific; a prediction tool that silently imports foreign legal assumptions could mislead rather than assist.

Explainability presents perhaps the deepest technical and philosophical problem. Deep neural networks, including the large language models now flooding into legal applications, make predictions through billions of weighted parameters in ways that resist human interpretation. Yet legal reasoning demands justification. A judge must explain a ruling; a lawyer must argue a position; a litigant is entitled to understand why they won or lost. An AI system that predicts an outcome without articulable reasoning cannot be audited, appealed, or trusted. The review argues that robust explainability mechanisms are not optional accessories but prerequisites for any legitimate deployment of AI in adjudication, and identifies their development as a central direction for future research.

Then there is the hallucination problem, the well-documented tendency of large language models to generate fluent, confident, and entirely fabricated content. In general-purpose chatbots, hallucination is embarrassing; in litigation, it can be professionally catastrophic. The review explicitly flags the risk of hallucination in legal AI systems as a persistent challenge, and the concern is easy to understand: a model that invents a plausible-sounding but nonexistent case citation, misstates a statute, or fabricates a contractual clause could corrupt legal proceedings in ways that are difficult to detect. Because legal outputs carry presumptions of authority, errors do not merely misinform, they can propagate into official records.

Regulatory uncertainty compounds the technical difficulties. Courts and bar associations across the world are still formulating rules about when AI may be used, what disclosure obligations apply, and who bears responsibility when an algorithm errs. The review notes that this regulatory fog slows adoption even where the technology is mature, because practitioners cannot confidently assess their professional liability. Cybersecurity adds another layer of risk: litigation files contain deeply sensitive personal and commercial information, and any AI system processing them becomes an attractive target for attackers, motivating the privacy-preserving learning frameworks the authors call for, techniques that allow models to learn from sensitive data without exposing it.

Looking forward, the review stakes out a clear research agenda. The authors argue that future progress depends on domain-specific, multimodal models rather than general-purpose systems adapted after the fact, on explainability mechanisms robust enough for judicial scrutiny, on privacy-preserving learning frameworks that protect litigants, and on clearer regulatory guidance that gives developers and practitioners a stable rulebook. Multimodality is particularly significant because legal evidence is not text alone; it includes photographs, audio recordings, video, and physical documents, and systems that can reason across these formats would more closely mirror how human tribunals actually weigh evidence.

The authors are candid about the limitations of their own synthesis, noting potential bias in their search, retrieval, and selection strategy as well as in database and language selection, a reminder that even systematic reviews filter the evidence they see. Still, the value of the work lies in its structure: for researchers designing the next generation of legal AI, for practitioners deciding which tools to trust, and for policymakers drafting the rules that will govern algorithmic justice, the review provides a shared reference point. As large language models continue their rapid advance, the question is no longer whether AI will shape litigation but whether the legal profession can build the safeguards, transparency, and governance needed to ensure that the technology strengthens, rather than undermines, the promise of fair and accessible justice.

Subject of Research: Applications, challenges, and future directions of artificial intelligence in litigation

Article Title: Artificial intelligence in litigation: a systematic review of applications, challenges, and future directions

Article References: Zhao, Z., Jebur, M. A., & Alzubaidi, L. (2026). Artificial intelligence in litigation: a systematic review of applications, challenges, and future directions. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11697-1

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11697-1

Keywords: artificial intelligence, litigation, legal AI, large language models, natural language processing, online dispute resolution, judgment prediction, explainability, systematic review, legal technology, transformers, hallucination