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AI incident #976 ·

AI-Generated OB-GYN Health Influencers on TikTok Used Fake Medical Credentials to Promote Dubious Advice

Open on the AI Incident Database 5 news reports Snapshot 7 Sep 2026

What happened

AI-generated avatars posing as OB-GYNs on TikTok, part of the so-called, crudely termed "Coochie Doctor" trend, have been falsely claiming years of experience while promoting dubious health advice. Many, including an avatar named "Violet," were created using the Captions app, which generates scripted AI influencers.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (5)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. AI-generated 'doctors' are scamming TikTok with fake credentials and bad advice
    dailydot.com · Lindsey Weedston · AIID #4984
  2. AI-generated ‘doctors’ are duping TikTok users with fake medical advice — here’s how to spot a ‘horrifying’ fraud
    nypost.com · Brooke Kato · AIID #4983
  3. AI-generated 'doctors' share fake credentials and questionable advice on TikTok
    indy100.com · Becca Monaghan · AIID #4985
  4. 건강 조언 믿었는데…10년 이상 경력 의사라는 여성의 정체 ‘충격’
    mk.co.kr · 이상규 기자, Sang-kyu Lee · AIID #4935
  5. Warning over ‘horrifying’ AI-trend becoming increasingly common
    news.com.au · Brooke Kato · AIID #4986

Who was involved

Alleged deployer
Unknown Tiktok Users
Alleged developer
Captions
Alleged harmed party
Tiktok Users

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 3.1.

  • Pursuing Consistent Context

    "LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Knowledge Gaps

    "Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Hallucinations

    "LLMs generate nonsensical, untruthful, and factual incorrect content"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Faithfulness Errors

    "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Defective Decoding Process

    In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Factuality Errors

    "The LLM-generated content could contain inaccurate information" which is factually incorrect

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Noisy Training Data

    "Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

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