By: Kanon Clifford

Evidence review in Ontario litigation, especially personal injury files, has always been a story of volume, time pressure, and judgment under uncertainty. Medical records arrive in thousands of pages; accident benefit files contain years of adjuster notes, insurer correspondence, and surveillance logs; and digital communications (texts, emails, social media) routinely expand the actual surface area of a claim. The practical problem is not merely “finding documents,” but assessing meaning: what happened, when, why it matters legally, and how reliably it can be proven.

Artificial intelligence (AI) is increasingly reshaping that assessment process. In contemporary litigation practice, AI is less a futuristic replacement for legal reasoning than an evidence triage and analysis layer, one that helps counsel locate, classify, summarize, and test the coherence of the factual record at scales that manual review struggles to match. At the same time, Ontario counsel must integrate these tools within professional competence duties, procedural proportionality norms, and evidentiary reliability requirements.

From e-Discovery to “e-Evidence Assessment”.

AI first gained mainstream legitimacy in litigation through technology-assisted review (TAR) and predictive coding, machine-learning workflows used to prioritize likely relevant documents for human review. Foundational empirical work demonstrated that TAR can outperform exhaustive manual review on measures such as recall and precision, while reducing effort and cost (Grossman & Cormack, 2011). This matters in Ontario, where discovery planning and proportionality are not abstract ideals but daily constraints in real files.

Canadian practice guidance has also evolved. The Sedona Canada Principles (Third Edition) explicitly frame electronic discovery around proportionality, defensibility, and appropriate technology use, recognizing that modern disputes involve diverse data sources and that parties must manage them responsibly (The Sedona Conference Working Group 7, 2022). 

Ontario courts have signaled openness to electronic document management and the recoverability of reasonable eDiscovery expenditures. Commentary summarizing Harris v. Leikin Group Inc. notes that judicial recognition of the need for efficient modern litigation with large document sets often requires electronic document management systems, with cost consequences flowing from that reality (BLG, 2021). 

But the more interesting shift is how AI is moving beyond “document production” into evidence assessment itself, helping litigators build and pressure-test the factual theory of the case.

Practical Transformations in Personal Injury Evidence Review

In Ontario personal injury and litigation practice, AI-enabled review tends to create value in five recurring ways:

1) Intelligent triage of large records.

Medical and rehabilitation records often include duplicative material, administrative pages, and clinically irrelevant content mixed with key evidence (diagnostic findings, functional limitations, causation-relevant timelines). AI classification and clustering tools can separate record types (e.g., imaging, consult notes, therapy reports), prioritize clinically salient passages, and flag missing intervals in treatment history. The result is not “automated conclusions,” but faster access to the evidentiary core that counsel must interpret.

2) Timeline construction and discrepancy detection.

AI extraction can identify dates, providers, locations, medications, work restrictions, and symptom descriptors, then assemble a chronological narrative. This is particularly useful when credibility and causation turn on consistency across sources (clinical notes, examinations for discovery, surveillance, employment records). Where accounts diverge, AI can highlight contradictions that merit lawyer-led investigation.

3) Pattern analysis across communications.

Text messages, emails, and platform chats are frequently central to liability disputes and quantum assessment. AI can support thematic analysis (e.g., admissions, planning, activities, symptom reports) and identify conversational context that a keyword search might miss. This improves recall while reducing the risk that critical context is buried in message volume.

4) Scalable quality control for productions.

Even with TAR, counsel remain responsible for defensible disclosure decisions. AI-assisted workflows can sample and validate review consistency, reducing the likelihood of systematic under-production or inadvertent disclosure. This aligns with the “process defensibility” lens emphasized in Canadian eDiscovery guidance (The Sedona Conference Working Group 7, 2022). 

5) Expert-facing preparation.

In personal injury files, experts are central. AI can help counsel prepare cleaner, more structured records for medical, vocational, and economic experts, while also supporting counsel’s own critique of assumptions, methodologies, and factual premises. Importantly, this is where legal standards for admissibility and reliability re-enter the frame.

The Legal Guardrails: Admissibility, Authenticity, and Professional Duties

AI may accelerate analysis, but it does not relax evidentiary requirements. Two sets of constraints matter most: (1) the law of evidence for electronic material and “AI outputs,” and (2) professional responsibility.

Authenticity and integrity of electronic evidence.

Canadian evidence law places the burden of proving authenticity for electronic documents on the party seeking to admit them (Canada Evidence Act, s. 31.1). The “best evidence” rule for electronic documents is satisfied through proof of integrity of the electronic documents system in which the record was stored (Canada Evidence Act, s. 31.2). In practical terms, if an AI system is used to transform evidence (e.g., transcribe audio, enhance video, extract metadata, summarize records), counsel should anticipate questions about chain of custody, process reliability, and reproducibility.

Novelty and expert evidence discipline.

Where AI-derived analysis becomes something more than an internal work product, particularly if tendered through an expert, Canadian courts apply admissibility frameworks designed to screen for necessity, relevance, and reliability. The Supreme Court of Canada’s expert evidence gatekeeping

begins with R. v. Mohan (1994) and has been refined by later guidance emphasizing independence and impartiality (e.g., White Burgess Langille Inman v. Abbott and Haliburton Co., 2015). If AI tools influence an expert’s opinion, parties should be prepared to explain (at least at a functional level) 

what the tool did, what data it relied on, and what validation steps were taken; the “black box” problem can become a reliability problem.

Privacy and personal information governance.

Personal injury files are privacy-intensive. Canadian privacy guidance for lawyers stresses careful management of personal information and addresses how privacy obligations may arise in litigation contexts (Office of the Privacy Commissioner of Canada, 2011). Even when litigation privilege and procedural rules support collection and use, AI vendors and cloud workflows raise practical questions: where data is stored, who can access it, how models are trained, and what logs are retained.

Professional competence and technology.

Ontario’s professional conduct framework now explicitly connects competence to technology. The Law Society of Ontario’s commentary states that maintaining competence includes developing understanding and the ability to use relevant technology, including appreciating benefits and risks and protecting confidentiality (Law Society of Ontario, 2022). Related LSO practice management guidance encourages lawyers to consider technologies that support timely, cost-effective client service while addressing security and systems risks. 

A Business Lens: Why This Is a Governance Shift, Not Just a Tool Upgrade

From a technology-law-business perspective, AI in legal review is best understood as a governance transformation: reallocating effort from low-value sorting toward higher-value judgment, while increasing the importance of controls. In business terms, AI improves throughput and reduces cycle time but also raises model, vendor, and compliance risks. The firms and litigation teams that benefit most tend to treat AI as part of an “evidence operations” system: documented workflows, audit trails, sampling-based QC, clear privilege protocols, and explicit accountability.

Final Remarks

AI is transforming how evidence is assessed by making review more scalable, structured, and analytically rigorous, particularly in document-heavy Ontario litigation and personal injury practice.  The real payoff is not automation of judgment, but improved access to the facts that drive judgment:  stronger timelines, more consistent review, faster identification of contradictions, and better expert preparation.

The equally important point is that AI’s value depends on disciplined deployment.  Authenticity and integrity remain legal thresholds; expert evidence remains subject to reliability screening; privacy remains a constant constraint; and Ontario lawyers’ competence duties now expressly include technology understanding. Used with governance and humility, AI can elevate evidence assessment from a time-consuming bottleneck into a strategic advantage, without compromising the standards that make evidence persuasive in court.

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About The Author

Kanon Clifford is a personal injury litigator at Bergeron Clifford LLP, a top-ten Canadian personal injury law firm based in Ontario. In his spare time, he is completing a Doctor of Business Administration (DBA) degree, with his research focusing on the intersections of law, technology, and business.

References

  • BLG. (2021, December 14). Recovering eDiscovery costs in Canada is the rule, not the exception. Canada Evidence Act, RSC 1985, c C-5, ss 31.1–31.2. (2026).  
  • Grossman, M. R., & Cormack, G. V. (2011). Technology-assisted review in e-discovery can be more effective and more efficient than exhaustive manual review. Richmond Journal of Law & Technology, 17(3).  
  • Law Society of Ontario. (2022). Rules of Professional Conduct—Chapter 3 (Commentary). Law Society of Ontario. (2020). Technology Guideline.
  • Office of the Privacy Commissioner of Canada. (2011). PIPEDA and your practice: A privacy handbook for lawyers (Guidance document).
  • The Sedona Conference Working Group 7 (Sedona Canada). (2022). The Sedona Canada Principles addressing electronic discovery (3rd ed.). The Sedona Conference.
  • R. v. Mohan, [1994] 2 SCR 9.
  • White Burgess Langille Inman v. Abbott and Haliburton Co., 2015 SCC 23.