Over the past year, healthcare providers have seen a marked rise in complaints that appear unusually polished, lengthy, and legally structured. Increasingly, these letters are being drafted—partially or entirely—using AI systems such as ChatGPT, Gemini, and copy-and-paste legal templates circulating online.

For clinics, this presents a new layer of complexity. AI-generated complaints often sound authoritative, use legal terminology inaccurately, and introduce narrative elements that do not exist in the medical record. While the tone may appear compelling, the evidential value is often very low.

As external adjudicators, HSCAMP is now routinely identifying AI-crafted complaint documents. This article explores how AI usage can be detected, its legal implications, and how lawyers and clinics should break down such complaints to neutralise exaggerated or inaccurate claims.

1. Why Patients Are Using AI to Draft Complaints

Patients increasingly turn to AI for:

• Refining emotional narratives into formal-sounding allegations
• Adding medical terminology, they don’t fully understand
• Structuring complaints like legal arguments
• Escalating minor dissatisfaction into quasi-legal claims
• Generating lists of complications or risks that never occurred

The problem:
AI does not know the patient, the procedure, or the clinical reality. It “fills gaps” using assumptions. This can result in complaints that sound compelling but contain:

✔ irrelevant medical concepts
✔ complications unrelated to the treatment
✔ invented timelines
✔ inconsistencies with contemporaneous notes
✔ invented regulations or misused legal terms

This disconnect becomes a powerful point in the provider’s defence.

2. How to Identify AI-Generated Complaints

Clinically, we see recurring markers that strongly suggest AI involvement:

1. Overly formal, polished language

Phrases such as “This raises significant concerns regarding duty of care,” “failure of informed consent,” or “the standard of practice expected by a reasonable body of practitioners” are common outputs from public AI models—not patients.

1. Legal terminology used inaccurately

Examples:

• Misusing "negligence" to describe dissatisfaction
• Stating “breach of GMC guidelines” when no guideline applies
• Confusing “non-dissolvable filler” with “permanent implant”

1. “Laundry lists” of risks unrelated to the actual treatment

AI tends to generate lists such as:

• granulomas
• abscesses
• vascular occlusion
• lymphatic obstruction
  — even when these are clinically irrelevant to the specific product or technique.

1. Sudden shift in writing style within the same complaint

Human-written paragraphs followed by AI-generated sections often have:

• different tone
• different sentence structure
• abrupt elevation in vocabulary

1. Descriptions that do not match medical records

Timelines rewritten, symptoms exaggerated, or medical phrases introduced that no clinician ever documented.

1. Repetition of phrases commonly generated by AI

E.g. “on balance of probabilities,” “holistic duty of care,” “this raises safeguarding concerns,” “this requires accountability and transparency.”

3. Legal Reality: AI-Enhanced Complaints Do NOT Strengthen the Case

Lawyers and adjudicators place very little weight on AI-embellished narratives.

Key legal principles:

1. Evidence must correlate with contemporaneous records

Medical notes, consent forms, and clinician documentation carry the highest evidentiary weight.

AI-written allegations that diverge from the notes are easily dismissed.

1. Complaints drafted by AI do not prove breach of duty

The Bolam, Bolitho, and Montgomery frameworks rely on:

• clinician behaviour
• accepted practice
• documented consent discussions

AI cannot retrospectively rewrite any of these.

1. AI-produced “medical reasoning” has zero probative value

Because it is not written by a clinician, it does not meet professional, evidential, or expert-witness standards.

1. Lawyers will dissect the complaint by comparing each allegation to:

1. Medical record entries
2. Consent documentation
3. Product literature
4. Post-procedure communication
5. Photographic evidence
6. Timeline consistency

If the AI-generated narrative fails these tests—which it usually does—the allegation weakens.

4. How Lawyers & HSCAMP Break Down AI-Generated Complaints

When HSCAMP receives a complaint for independent adjudication, the first step is a forensic breakdown of the document.

We categorise it as follows:

1. Factual statements

What is verifiably true (dates, treatments, notes).

2. Subjective statements

Feelings, preferences, dissatisfaction—valid but not probative.

3. AI-fabricated or AI-exaggerated elements

These include:

• invented complications
• timelines that conflict with the clinical notes
• terminology the patient could not reasonably have known
• allegations unsupported by photographic evidence
• theoretical risks listed as if they occurred

These are highlighted and reviewed separately.

4. What is missing

AI often produces lengthy narratives but fails to mention:

• exact dates
• exact symptoms
• exact interactions
• what the patient actually wants
• any correlation to the product injected

These omissions are obvious to investigators.

5. Why AI-Assisted Complaints Often Undermine The Patient’s Position

Because the inconsistencies become more prominent.

A short, honest complaint may raise real issues.

A long AI-embellished complaint often:

• introduces contradictions
• creates claims that can be disproven
• damages the patient’s credibility
• highlights discrepancies with notes
• forces scrutiny on irrelevant allegations
• increases suspicion of bad-faith escalation

When the AI narrative fails to match reality, the entire complaint weakens—legally and factually.

6. How Clinics Should Respond (and Protect Themselves)
7. Do not respond emotionally

AI-crafted letters often have a confrontational, accusatory tone. This is stylistic, not personal.

2. Anchor every response to the medical record

Facts defeat AI-generated fiction.

3. Point out inconsistencies politely and professionally

E.g.
“The complaint references risks such as X, Y, Z. These are not associated with the product used, as confirmed by the product literature and medical records.”

4. Highlight where allegations conflict with contemporaneous notes

Courts give overwhelming weight to clinical documentation.

5. Escalate to your external complaints body (HSCAMP)

This ensures:

• impartiality
• defensibility
• procedural correctness
• professional adjudicator allocation

6. Maintain clear, high-quality medical notes

The single most effective defence against all complaints—AI-generated or otherwise.

7. The Future: AI Will Become Common in Complaints—But It Won’t Replace Evidence

Healthcare complaints are entering an era where AI amplification is normal. But the fundamentals remain unchanged:

• Medical records determine the outcome.
• Consistent documentation protects clinicians.
• Legal tests still rely on professional standards, not linguistics.
• External adjudicators and insurers are well-versed in spotting AI patterns.

AI can make a complaint sound stronger.
It cannot make it factually or legally stronger if the allegations do not correlate with clinical reality.

Conclusion

AI-generated complaints are now part of the healthcare landscape. Clinics must not be intimidated by the length, tone, or legalistic wording. When analysed correctly, these documents usually reveal:

• inconsistencies
• invented risks
• misunderstandings
• inaccuracies
• poor alignment with clinical notes

By identifying AI involvement early, dissecting the narrative carefully, and anchoring every response to factual documentation, healthcare providers and their legal representatives can neutralise exaggerated complaints effectively and professionally.

HSCAMP’s adjudication pathways are specifically designed to manage this new wave of AI-enhanced complaints—ensuring fairness, objectivity, and evidence-based conclusions.