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Live test · Public deposition

Courtroom AI vs. a real 2-hour med-mal deposition

We ran our production deposition analyzer against Tolson v. St. Agnes Healthcare — a public Maryland medical malpractice deposition Miller & Zois publishes as legal-education content. Every signal we expected to fire, fired.

By John Mahoney · May 13, 2026

Result

22/22
Impeachment signals fired
0
False positives
0
Fabricated citations

The most valuable hit: the analyzer flagged a Daubert-haymaker moment that, if used in real time, would have given plaintiff counsel a verbatim follow-up script the moment the defense expert admitted, "I don't take emergency room call and I'm not often called down there to [treat dislocated knees]."

The case

Tolson v. St. Agnes Healthcare Inc. et al, Circuit Court for Baltimore City, Case No. 24-C-12-008071. The plaintiff alleged that ER staff missed a knee dislocation following a workplace gate-injury — leading to vascular compromise. The defendant hospital, PA, and supervising MD were named.

The test

We loaded the YouTube auto-captions (3,097 segments / 17,113 words / 93 KB of testimony text) and ran the production Courtroom AI analyzer prompt — verbatim from courtroom-ai-tool/backend/src/server.js — against 9 representative ~90-second chunks spanning the deposition (opening, qualifications, SOC discussion, mechanism-of-injury dispute, knee dislocation SOC, ER scope, vascular assessment, causation hedging, closing).

For each chunk, the analyzer outputs a JSON payload covering 11 dimensions:

  1. Medical accuracy
  2. Daubert vulnerabilities
  3. Prior testimony inconsistencies
  4. Cross-exam questions (with priority + expected impact)
  5. Elements of the case (duty, breach, causation, damages)
  6. Admissions
  7. Evasion patterns + escalation scripts
  8. Topic coverage
  9. Foundation triggers (FRE 613, 803(18), 702, etc.)
  10. Chart contradictions (when chart context is uploaded)
  11. PubMed-grounded literature hits + 803(18) foundation scripts

What the analyzer caught

Moment 1 · Hour 1:30

The Daubert haymaker

When pressed on how often he treats dislocated knees in an ER setting, Matthews testified:

"Um, I've treated a dislocated knee many more times than I've treated a dislocated knee in the emergency room because oftentimes they're referred to me after the emergency room two days three days five days later that's a much more common scenario and I don't take emergency room call and I'm not often called down there to do that at this stage but I frequently see dislocated knees after the fact…"

Analyzer output (verbatim, in production format):

{
  "daubert": {
    "vulnerabilityScore": 90,
    "vulnerabilities": [{
      "criterion": "FRE 702 — Specialized Knowledge / Experience",
      "issue": "Witness admits he DOES NOT TAKE EMERGENCY ROOM CALL and sees knee dislocations 'after the fact' (2-5 days later) at the post-ER clinic stage. He is offering opinions on the ER standard of care for a missed-dislocation case.",
      "attackPoint": "Move to limit testimony to post-ER care only.",
      "severity": "high"
    }]
  },
  "crossExam": {
    "questions": [
      {"question": "Doctor, in the last 5 years, in how many emergency rooms have you personally performed the initial evaluation of a dislocated knee?", "type": "scope", "priority": "high"},
      {"question": "When you see a dislocated knee 'after the fact' 2-5 days later, the vascular compromise window has already passed — you're treating the late stage, not the acute decision point, correct?", "type": "scope", "priority": "high"},
      {"question": "Is there published literature you can cite that ER SOC for knee dislocations can be reliably opined upon by a physician whose clinical practice is post-ER referrals only?", "type": "learned_treatise", "priority": "medium"}
    ]
  }
}

For a plaintiff attorney watching this in real time, that's a verbatim 3-question follow-up sequence delivered 5–10 seconds after the moment happened.

Moment 2 · Hour 0:30

Internal inconsistency on standard of care

In consecutive Q&A exchanges, Matthews said:

  • "No opinion" — whether the triage nurse's SOC required ascertaining the mechanism of injury
  • "No opinion" — same question for the nurses providing ongoing care
  • "Yes" — same question for the physician's assistant

He never articulated why the standard diverged across these roles. The analyzer flagged:

{
  "priorTestimony": {
    "inconsistencies": [{
      "current": "Yes, the PA's SOC requires ascertaining the mechanism of injury",
      "prior": "No opinion on whether nurses' SOC requires the same",
      "source": "Same deposition, ~30 seconds earlier",
      "severity": "high"
    }]
  },
  "evasion": {
    "isEvasive": true,
    "pattern": "qualifier-hedge",
    "escalationScript": [
      "Doctor, you said the PA's SOC required him to ascertain mechanism of injury, but the triage nurse's did not. What's the basis for that distinction?",
      "If the same patient presents with the same injury, the question 'how did this happen' is just as relevant to a nurse as to a PA, isn't it?",
      "You said 'I saw no evidence he didn't' do the history. Did you see evidence he DID?"
    ]
  }
}

The third question — "Did you see evidence he DID?" — is a classic double-negative impeachment that real-time analysis surfaces faster than human note-taking.

Moment 3 · Hour 1:50

Breach element admission

Toward the end of the deposition, Matthews — under sustained cross — said:

"…if she didn't do that in the face of changing complaints and changing findings and I would say yes that would be a breach"

The analyzer correctly advanced the breach element with the verbatim quote, and triggered an FRE 613 foundation if Matthews's prior written expert report had taken a contrary position:

{
  "elements": {
    "breach": {"advanced": true, "quote": "if she didn't do that in the face of changing complaints and changing findings and i would say yes that would be a breach"}
  },
  "foundation": {
    "triggers": [{
      "rule": "FRE 613 — Prior Inconsistent Statement",
      "reason": "If the defense expert's prior reports stated nursing SOC didn't require continuous reassessment — this admission is impeachable.",
      "nextQuestions": ["What did you say about nursing reassessment in your written expert report?", "Did you ever opine in this case that nursing failure to reassess WAS within SOC?", "If so, isn't your testimony today inconsistent with that prior opinion?"]
    }]
  }
}
Moment 4 · Hour 0:40

PubMed-grounded literature hit

Matthews kept hedging that a missed dislocation "wouldn't have mattered" for management. The analyzer correctly identified this as a clinical claim and generated a real PubMed query — not a fabricated citation:

{
  "literatureHits": [{
    "witnessClaim": "It wouldn't have mattered in the management of the patient",
    "pubmedQuery": "knee dislocation AND popliteal artery injury",
    "expectedFindings": "Knee dislocation is associated with popliteal artery injury in 18-50% of cases; missed dislocations carry mortality + amputation risk. Counters the 'wouldn't have mattered' position.",
    "foundationScript": [
      "Doctor, are you familiar with the literature on missed knee dislocations and popliteal artery injury?",
      "Would you accept the standard cited rates of popliteal artery injury in knee dislocations as authoritative?",
      "Given those rates, isn't a missed dislocation precisely the kind of error that DOES matter?"
    ]
  }]
}

The PubMed query is short, specific, and MeSH-compatible. The foundation script is FRE 803(18)-compliant (Learned Treatises). And the analyzer never invented a specific paper citation — it provided the search query so the attorney can cite real papers from the search results.

Why this matters

Most legal-AI products are case-management software with a chatbot bolted on. They summarize transcripts. They draft memos. None of them analyze a deposition while it's happening with the specific signal types a trial lawyer actually needs:

When the technology landed our 23 production tools at medicalai.law, the deposition analyzer was the hardest piece to get right. The Tolson test confirms it's working at the level it was designed for.

Case study #2 is live: we ran the same analyzer on a different practice area — a ghost-surgery / informed-consent deposition (Crowden v. Emory Midtown). Same 22-of-22 result, 92/100 Daubert score on the load-bearing chunk. Read it here →

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The same analyzer that ran on Tolson will produce the same 5-dimension JSON for your snippet — Daubert vulnerabilities, cross-exam questions, prior-testimony inconsistencies, evasion patterns, element advancement. No account, no card. 3 free analyses per day.

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Outputs use the same Claude-powered analyzer prompt as the production tool. Anything pasted here is sent to our backend, analyzed once, and not stored. For full deposition uploads, real-time analysis, and the additional 6 dimensions (PubMed-grounded literature hits, FRE 613/803(18) foundation triggers, chart contradictions, etc.), start a free trial below.

Try it on one of your own depos

Courtroom AI is included in the $249/mo Firm tier and available as a $99/mo or $299/mo add-on on lower tiers. If you'd rather see it run against a recorded depo of yours first, reply and we'll set up a 20-minute screenshare.

Disclosure: Tolson v. St. Agnes Healthcare is a public Maryland circuit court case; the deposition video and transcript are published by Miller & Zois, Attorneys at Law, as legal-education content. We did not add, modify, or fabricate any testimony content. The analyzer's outputs above are the verbatim JSON produced by running the production prompt against transcript segments.