See how AI medical-record review links every fact to the exact Bates page that proves it — click any citation and jump straight to the record.
See the 60-second demo →Mata v. Avianca (S.D.N.Y. 2023) — the case that launched a thousand law-review articles — was the first major AI-sanctions case in U.S. legal practice. Two attorneys submitted a brief citing six fabricated case decisions that ChatGPT had invented wholesale. They were fined $5,000 each and the case became required reading. Since then, the sanctions docket has grown. By 2026, sanctions orders related to fabricated AI citations are reported approximately monthly across federal and state courts.
For plaintiff medical malpractice attorneys, AI is now indispensable for record review, chronology building, and expert workup. But the AI-sanctions risk is real, and the patterns that drive it are specific. This is the catalog of the three mistakes that drive the sanctions docket — and the workflow safeguards that make AI safe to use in court.
This is the Mata v. Avianca mistake — and it has not gone away. In every AI-sanctions case we've tracked through 2026, fabricated case citations are the proximate cause. ChatGPT, Claude, Gemini, and other general-purpose LLMs can produce realistic-looking case citations — court name, parties, reporter, page number, parenthetical — that are entirely fabricated. The citations don't exist. The opinions don't exist. The reasoning attributed to them is invented.
How it happens: Attorney asks ChatGPT (or similar) to "find me a Third Circuit case on Daubert reliability of differential diagnosis." ChatGPT produces a citation that looks correct. Attorney copies it into the brief. Attorney never opens the cited case. Defense (or the court) tries to look it up. The case doesn't exist.
Why it still happens: Plaintiff attorneys are busy, deadlines are tight, the AI output looks polished, the attorney's confirmation bias does the rest. The sanctions cases all share this pattern: the attorney didn't check the citation.
This is the next-generation sanctions risk, and we expect it to drive the bulk of 2026-2027 cases. AI tools can produce expert-report-quality output — methodology articulation, alternative-cause analysis, peer-reviewed citations, even draft opinions. The temptation is to use the AI output as the report itself and have the expert sign it after a cursory read.
How it happens: Attorney runs the case through a Daubert workup tool. The tool produces a draft expert report including draft opinions. Attorney shares the draft with the retained expert. Expert reviews quickly, signs. Report is filed. Defense moves to exclude under FRE 702 amendment, arguing the expert is not actually the author of the methodology articulation.
What courts are doing: The 2023 FRE 702 amendment requires the expert to have actually applied the methodology. If the AI did the application and the expert merely signed off, the expert can be cross-examined into admitting the methodology articulation wasn't theirs. Result: exclusion on application-to-facts grounds, possible Rule 26 sanctions if the AI authorship wasn't disclosed.
This isn't a sanctions risk per se — it's a HIPAA violation risk and a potential bar discipline risk for protecting client confidences. But it's the most common AI mistake we see plaintiff attorneys make, and the consequences can be severe.
How it happens: Attorney has a 1,500-page medical record production. Attorney pastes it into ChatGPT (consumer version) and asks for a chronology. The records contain the client's name, DOB, diagnoses, treatments, and provider information. The consumer ChatGPT terms of service permit OpenAI to use that input for training. The PHI has now been transmitted to a non-BAA vendor and is potentially being used to train a future model.
Why it still happens: The attorney didn't realize the consumer ChatGPT vs the enterprise ChatGPT have different data-handling terms. The attorney thought "AI is AI." The attorney was solving a real problem (1,500 pages is a lot) and reached for the most convenient tool.
A growing number of federal and state courts now require attorneys to disclose AI use in filings — local rules in the Northern District of Texas, multiple judges in the Southern District of New York, and Sixth Circuit standing orders. The disclosure usually requires:
This is not yet uniform. Check your jurisdiction's specific rules and the standing orders of each judge before each filing. Some judges go further and require disclosure of the specific tool used.
Defense firms in 2026 are increasingly using AI disclosure as a discovery tactic. Interrogatory requests like "Identify every AI tool used to prepare any document produced in this litigation" are common. The aim is to either (a) catch fabricated citations through deposition, (b) attack expert reports as AI-authored, or (c) pressure plaintiff firms into not using AI at all.
The protective practice is the same as the safeguards above: use only HIPAA-compliant, citation-grounded tools; document expert review independently; verify every citation manually. Done right, AI use is fully defensible.
Four properties to look for in any legal AI tool you use in active litigation:
AI sanctions in 2026 are real but predictable. They happen when attorneys (a) cite AI-generated authority without verification, (b) treat AI output as the expert's own work, or (c) upload PHI to non-HIPAA-compliant tools. The defensible AI workflow is the inverse: PubMed-grounded citations, expert-augmenting drafts, BAA-protected processing. Done right, AI is a force multiplier that survives any defense Daubert motion or sanctions inquiry.
Try MedLegal AI's free Daubert workup — PubMed-grounded, BAA-compliant, expert-augmenting by design.
MedLegal AI is software, not a law firm. We do not provide legal advice. All AI-generated outputs require independent review by a licensed attorney.
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