By the end of this tutorial, every statement remaining in your chronology or report will be traceable to a real, relevant document. You will know how to use linked references, Search, and AI Chat to catch unsupported facts, misread names, irrelevant citations, blurred objective and subjective language, missing or misdirected references, and contradictions between documents.

Every statement an AI generates in a chronology, or report can look equally confident, whether it's drawn directly from a document or quietly assumed. That confidence is exactly what makes unchecked AI content risky. Catching issues during review, protects the accuracy of the case. This tutorial builds the habit of treating every AI-generated statement as a claim to verify, not a fact to accept.

The list below covers the most common problems with Checking AI-Generated Statements. Each entry follows the same pattern: what you will notice, why it likely happened, and how to fix it.

Problem: The AI-generated statement claims more than the source document shows.

Likely cause: AI filled a gap in the record by connecting two events, inferring a cause, or stating a conclusion that the documents only suggest.

Example:

Source: "Pt reports low back pain, denies radiation. Denies numbness/tingling. Tenderness on palpation of lumbar paraspinal muscles. Impression: lumbar strain."

AI-generated statement: "The patient presented with severe low back pain radiating into the lower extremities, with associated numbness, consistent with a lumbar disc injury likely caused by the fall."

The AI added a severity qualifier the clinician never used, reversed two denied symptoms into confirmed ones, upgraded a documented lumbar strain to a disc injury, and inserted a causation opinion the clinician never gave. Left uncorrected, it would undermine the credibility of the whole report.

Fix:

Problem: The name of a provider, facility, procedure, or diagnosis doesn't match the source.

Likely cause: Handwriting, abbreviations, or a poor scan led the AI to misread a name, merge two similar facilities, or alter a diagnosis or procedure.

Example:

Source: "s/p ORIF (L) distal radius."

AI-generated statement: "Patient underwent ORIF of the right distal radius."

The AI misread the handwritten "(L)" as "(R)," reversing which wrist the surgery was performed on. Uncorrected, this one-letter error would look less like a typo and more like a factual inconsistency in the whole case, leaving you to question whether the chronology was tracking the right injury at all.

Fix:

Problem: The statement is accurate, but it has nothing to do with the case's real issues.

Likely cause: The linked reference exists and is correctly described, but it does not bear on any issue actually in dispute.

Example:

For example, in a shoulder injury claim, a statement about the patient's childhood tonsillectomy might come straight from the intake form. It's accurate, but it has no bearing on liability, causation, treatment, or damages. The reference is real. The statement is still noise.

Fix:

Problem: The statement treats a patient's reported symptom as a confirmed medical finding.

Likely cause: AI states a subjective complaint using the same definitive language reserved for examination findings, imaging, or lab results.

Example:

Source: "Pt reports difficulty sleeping and low mood since the injury. He believes it might be depression."

AI-generated statement: "Patient's main diagnosis is depression secondary to the injury."

Fix:

Problem: The statement carries a reference, but it points to the wrong document.

Likely cause: The AI linked to a document that mentions the topic rather than the document that actually contains supporting details.

Example:

AI-generated statement: "Patient proceeded with 5 PT procedures."

Source: the patient's claim form, not the clinical records.

The claim form may mention that physical therapy occurred, but it can't confirm the number of sessions, the dates, or what was performed. The clinical PT reports exist elsewhere in the case and are the correct source, but the AI linked to the claim form instead. A reviewer opening the reference sees something related to the topic, not the record that fully supports the specific detail.

Fix:

Problem: The report presents only one version of the event, even though the underlying documents reflect several conflicting versions.

Likely cause: Different providers evaluated the same condition from different perspectives — a pattern especially common in expert medical evaluations — and the AI adopted one version without noting the other.

Example:

Treating clinician's note: "Onset of symptoms immediately following the accident."

Defense Independent Medical Examination report: "Patient reports symptoms began approximately two weeks after the accident."

AI-generated statement: "Symptoms began immediately following the accident."

The timing of symptom onset can affect whether an injury is linked to the incident at all. Two evaluators documented different timelines, and the AI presented only the treating provider's version as fact, without noting that the IME's record conflicts with it.

Fix:

Summary

Every statement remaining in your chronology or report is now backed by a real, relevant, and accurately described document. Names, procedures, and diagnoses match their sources, unsupported assumptions have been removed or corrected, objective findings and subjective complaints are kept distinct, and any conflicts between records are noted rather than settled without your review.