AI-Generated Medical Records: What Claims Teams Need to Know

Creating a convincing fake medical record once required extensive knowledge of clinical terminology, billing codes, and provider documentation practices. Today, generative AI has dramatically lowered that barrier. With a simple prompt, someone can create treatment notes, disability certifications, or diagnostic reports that closely resemble legitimate medical records.

For claims professionals, that changes what a “red flag” looks like. Obvious mistakes and poor formatting were once common indicators of fraud. Now, records that appear polished or complete may deserve a closer review.

What Fabricated Medical Documentation Can Look Like

AI-generated medical documentation can appear in several different forms throughout the claims process, making it important to recognize the various ways it may surface:

• Synthetic treatment notes: A claimant or provider may generate records documenting office visits, procedures, or diagnoses that never occurred while ensuring they fit a believable injury narrative.
• Fabricated disability and wage-loss documentation: Work-status forms, physician certifications, and return-to-work restrictions can be created to support an exaggerated or entirely fictitious claim.
• Fabricated prescription and billing records: Prescription histories, pharmacy fill records, and provider billing entries may be generated to support treatment that never happened. These documents often begin to fall apart when compared against pharmacy or billing data.
• Manipulated diagnostic imaging: X-rays and other diagnostic images can be generated or altered to depict injuries that don’t exist or exaggerate the severity of legitimate ones.

A study published in Radiology, the journal of the Radiological Society of North America, tested 17 radiologists using a mix of real and AI-generated X-rays. When participants were not told synthetic images were included, only 7 of the 17 (41%) recognized that something was unusual.

After being told to look for AI-generated images, their accuracy improved to approximately 75%—better, but still far from perfect. It’s also worth remembering that real-world claims reviews rarely come with advance warning that AI-generated images may be present.

Risks for Claims Teams

Medical documentation plays a critical role in nearly every claim. It supports causation, influences reserve-setting, and often determines whether a claim is approved, denied, or ultimately heads toward litigation.

An industry survey of insurance leaders found that 48% of North American carriers have already experienced an increase in suspicious or fraudulent claims involving AI-generated documentation, including forged invoices, altered repair estimates, and fabricated medical records.

For SIU teams, well-crafted fabricated records often don’t appear suspicious on their own. Instead of relying on how a document looks, investigators increasingly need corroborating evidence from outside the record itself. That shift creates several challenges:

• Reserve accuracy: Reserves based on fabricated treatment histories or false disability timelines are built on inaccurate information from the start.
• Claim decisions: Approvals or denials based on synthetic documentation become much more difficult to reverse once payments have been issued.
• Litigation exposure: If fabricated documentation is uncovered later through discovery or an SIU investigation, it can complicate an otherwise well-managed claim.

Red Flags Worth Watching For

No single indicator confirms that documentation has been fabricated. However, when several of these signs appear together, they’re worth investigating further and most can be identified without specialized technology.

Provider and treatment inconsistencies:

• Provider names, license numbers, or practice addresses that don’t match licensing board records or can’t be confirmed through a quick verification call.
• Treatment timelines that don’t align with the reported injury date or mechanism of injury.
• Clinical language that feels noticeably more polished or formal than the provider’s typical documentation style.

Cross-source and context mismatches:

• Records that don’t align with pharmacy fill history, billing codes, or diagnostic imaging already on file.
• Treatment notes referencing procedures or medications with no corresponding billing entries.
• Disability or wage-loss documentation that conflicts with employer-reported dates.
• Records submitted without supporting intake paperwork, referrals, or prior visit history.
• Documentation received as one complete package rather than the gradual, piecemeal manner in which provider offices typically produce medical records.

Verification Remains the Best Defense

Claims professionals don’t need to become AI experts to address this evolving risk. They simply need to make verification a consistent part of the claims review process.

A quick phone call to a provider’s office, a licensing board lookup, or a comparison against pharmacy and billing records may only take a few extra minutes, but those steps remain some of the most effective ways to confirm that documentation is legitimate.

As generative AI continues to make fabricated medical records more difficult to identify at first glance, verification is becoming less of a best practice and more of a standard part of sound claims handling. The claims professionals best equipped to identify medical record fraud won’t necessarily be the ones using the latest AI detection software; they’ll be the ones who consistently verify the information they’re given before making important claim decisions.

When documentation raises questions, experience matters. Connect with our experts to help verify medical records, uncover inconsistencies, and support confident claim decisions.

Check out our sources:

National Insurance Crime Bureau. “NICB Projects 49% Rise in Insurance Fraud Linked to Identity Theft in 2025.” National Insurance Crime Bureau, 2 Sept. 2025, www.nicb.org/news/news-releases/nicb-projects-49-rise-insurance-fraud-linked-identity-theft-2025.

“North American Carriers Warned of ‘Structural’ Claims Cost Shift in Gallagher Bassett Study.” Insurance Business, 17 Feb. 2026, www.insurancebusinessmag.com/us/news/claims/north-american-carriers-warned-of-structural-claims-cost-shift-in-gallagher-bassett-study-565555.aspx.

“The Rise of Deepfake Medical Imaging: Radiologists’ Diagnostic Accuracy in Detecting ChatGPT-generated Radiographs.” Radiology, 24 Mar. 2026, doi.org/10.1148/radiol.252094.