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Alleged Harmful Health Outcomes Following Reported Use of Purported ChatGPT-Generated Medical Advice in Hyderabad

2 news reports Snapshot 7 Sep 2026

In brief

An AI system built and deployed by Openai allegedly harmed Unnamed Patient With Diabetes In Hyderabad, Unnamed Kidney Transplant Patient In Hyderabad and 5 others.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
2 reportsNov 2025

What happened

Reports from Hyderabad describe two alleged patient harms after individuals acted on purportedly ChatGPT-generated medical advice instead of clinician guidance. A kidney-transplant recipient reportedly discontinued prescribed post-transplant medications based on a chatbot suggestion and experienced graft failure. In a separate case, a man with diabetes allegedly developed severe hyponatremia after following a chatbot-advised zero-salt diet.

Laws that address this harm

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (2)

Titles link to the original publisher; report text is not reproduced here.

  1. ChatGPT Isn't Your Doctor: Hyderabad Doctors Warn After Patients Harmed by AI Advice
    completeaitraining.com · Complete AI Training
  2. Doctors warn against relying on AI tools for medical advice
    timesofindia.indiatimes.com · Ajay Tomar

Who was involved

Alleged deployer
Openai
Alleged developer
Openai
Alleged harmed party
Unnamed Patient With Diabetes In Hyderabad, Unnamed Kidney Transplant Patient In Hyderabad, Openai Users, General Public Of India, General Public Of Hyderabad, General Public, Chatgpt Users

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
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Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Technical and operational risks

    "To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Openai

Source record: incident #1281 on the AI Incident Database · all 2 reports