AI chatbots can produce fluent, confident answers without verifying facts, understanding your full situation, or recognizing when a question requires professional judgment. In health and other high-stakes contexts, that combination can turn an apparently useful reply into bad advice. The safest approach is to treat chatbot output as an unverified starting point, not as a diagnosis, treatment decision, emergency service or substitute for a qualified person.
Why a chatbot can sound right while being wrong
Fluency is not evidence
Large language models generate likely sequences of words. They are optimized to produce coherent, helpful-sounding text, not to prove every statement before presenting it. A response can therefore have the tone, structure and vocabulary of an expert answer while containing a fabricated citation, an incorrect explanation or a dangerous omission.
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The World Health Organization (WHO) warned on 16 May 2023 that large language model responses can appear authoritative and plausible while being completely incorrect or seriously erroneous, particularly for health questions. Confidence in the wording is a property of the presentation, not a measurement of accuracy.
Training material is uneven
Models learn from very large collections that may contain outdated guidance, errors copied from one site to another, discriminatory language, commercial claims and informal advice. In medical settings, useful clinical information may also be absent from material that is freely available online. A model can reproduce a common misconception because it was frequent in its training data, even when a current professional guideline says otherwise.
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WHO has also identified the possibility that biased data will generate misleading or inaccurate content. The same systems can be misused to create convincing disinformation, making polished wording especially difficult for a non-specialist to evaluate.
They may answer before collecting the facts that matter
A clinician normally asks follow-up questions, checks a history, reviews medicines and considers timing, age, pregnancy status, allergies, examination findings and other risks. A chatbot often responds to the first short prompt. If the user leaves out a crucial detail, the model may silently assume a typical situation and give advice that is inappropriate for the actual one.
The physician-led HealthAdvice study noted that ordinary patient questions are commonly short, open-ended and written in everyday language. The authors described examples in which responses failed to account for comorbidities, medicines, pregnancy risk or uncertainty about what a child had swallowed.
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Small changes in wording can produce materially different answers. The HealthAdvice authors reported poorer performance when questions contained typos or misspellings. A response generated after a carefully specified prompt is not necessarily more medically sound; it may simply be more aligned with the assumptions expressed in that prompt.
Fabrication and excessive agreement
When a model lacks information, it can fill the gap with a plausible-sounding claim rather than clearly saying that it does not know. It can also agree too readily with a user’s premise, a behavior often called sycophancy. If the premise is false or the user is seeking reassurance for a risky decision, agreement can reinforce the mistake instead of challenging it.
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What a physician-led evaluation actually found
A 2026 HealthAdvice study tested four specific model versions with 222 patient-posed questions covering internal medicine, pediatrics and women’s health. Sixteen physicians rated 888 responses. Most responses were collected through public chatbot interfaces between 10 September and 24 December 2024; one saved response was collected on 12 February 2025.
| Model version tested | Problematic responses | Unsafe responses |
|---|---|---|
| Claude 3.5 Sonnet | 21.6% | 5% |
| Gemini 1.5 Flash | 27.5% | Not stated separately in the reported figures |
| GPT-4o | 31.5% | 13% |
| Llama 3.0/3.1-70B | 43.2% | 13% |
Across those four versions, problematic-response rates ranged from 21.6% to 43.2%, while unsafe-response rates ranged from 5% to 13%. These are results for the named versions, questions, prompts, dates and physician-rating framework. They are not a universal error rate for every chatbot, a guarantee of current performance or a prediction of how another question will be answered.
The study authors also cited an estimate that 43 million patients in the United States ask chatbots medical questions at least once per month. That figure came from sources referenced by the authors; it was not an original population estimate produced by this evaluation. Its significance is that even a modest failure rate can affect many decisions when people use chatbots at scale.
Why exam scores do not settle the safety question
A model can perform well on a structured test with a clearly defined answer and still fail on a real person’s underspecified question. Everyday advice may require a sequence of clarifying questions, recognition of an emergency and judgment about uncertainty. A benchmark that supplies all relevant facts does not test whether the system notices which facts are missing.
Why a question such as “my child has a fever” is difficult
A parent asking what to do about a fever may not mention the child’s age, temperature and measurement method, duration, breathing, alertness, hydration, rash, chronic conditions, recent medicines or exposure to illness. Those details can change whether home monitoring is reasonable or urgent assessment is needed.
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The same problem appears in adult questions. “Can I take this painkiller?” may require information about pregnancy, kidney disease, ulcers, anticoagulants, allergies and other medicines. “Is this symptom normal?” may depend on timing, severity and associated signs. A chatbot that gives a direct answer without obtaining those facts can create false reassurance or unnecessary alarm.
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Users can also provide inaccurate information, misunderstand a measurement or omit a detail they do not realize is important. A model has no independent examination unless a qualified person supplies and interprets the relevant data.
What “harm” includes, especially in mental-health tools
Unreliable information is only one category of risk. A 2026 scoping review grouped reported mental-health concerns into five areas:
- Psychological or psychiatric effects: possible effects on distress, symptoms or emotional dependence.
- Relational and behavioral effects: changes in human relationships, help-seeking or behavior.
- Cognitive and educational effects: effects on judgment, learning, attention or reliance on automated answers.
- Output-safety failures: responses that are unsuitable, unsafe, manipulative or inappropriate for a crisis.
- Governance, privacy and structural risks: data exposure, unequal performance, weak accountability and broader social effects.
The review is a map of proposed and reported concerns, not a finding that every harm is common. For many risks, the likelihood and real-world impact remain unclear. A named possibility should not be presented as proof that chatbot use caused a particular mental-health outcome.
Emotional vulnerability raises the stakes. A person in crisis may interpret an empathetic-sounding answer as professional support, disclose intimate information or delay contacting a human service. A general-purpose chatbot cannot reliably assess imminent danger, verify a user’s safety or provide the accountable follow-up that crisis care requires.
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Privacy, security and deployment failures
WHO has warned about sensitive-data exposure as well as misinformation. Entering names, medical records, account credentials, location details or another person’s private information can create risks that are separate from whether the generated answer is accurate. Before sharing anything sensitive, understand what the service stores, who can access it, how long it is retained and whether it may be used for model improvement.
A National Institute of Standards and Technology (NIST) National Cybersecurity Center of Excellence report documented a particular internal chatbot prototype. The threats it discussed included prompt injection, hallucinations, data exposure and unauthorized access. Its example mitigations included local deployment, access controls and validation filters. NIST explicitly described the document as a point-in-time prototype report, not implementation guidance, so it should not be treated as a universal security checklist.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to use a chatbot without outsourcing a high-stakes decision
- Classify the question first. Use extra caution for symptoms, medicines, self-harm, legal rights, financial loss, safety hazards, identity documents and decisions affecting a child or another vulnerable person.
- Ask for uncertainty and missing information. Request a list of facts that would change the answer, rather than accepting a single confident recommendation.
- Separate explanation from action. A chatbot may help define a term or generate questions for a professional; that is different from diagnosing a condition, choosing a dose or deciding that emergency care is unnecessary.
- Verify consequential claims. Check dates, dosages, contraindications, statistics and quotations against a current government, professional or institutional source. Follow the cited source itself, not merely a citation-looking sentence.
- Use a qualified human for decisions. A clinician, pharmacist, crisis service, lawyer or other appropriate professional can account for context and responsibility that a text generator cannot.
- Escalate emergencies immediately. Severe breathing difficulty, chest pain, signs of stroke, serious allergic reaction, poisoning, loss of consciousness, imminent self-harm or danger to another person require local emergency or crisis services rather than a chatbot conversation.
- Minimize personal data. Remove names, addresses, exact dates of birth, account numbers and identifying details unless there is a clear, understood reason to provide them.
- Watch for reassuring language that lacks a basis. Statements such as “you are definitely fine” or “this is certainly harmless” deserve the same verification as an obviously technical claim.
How to compare claims that one chatbot is safer
A ranking detached from its test setup is misleading. Compare systems on the same dimensions:
| Comparison question | Why it matters |
|---|---|
| Which exact model and version were tested? | Models change, and a result for one release does not establish performance for another. |
| When and where was it tested? | Interface updates, regional settings and collection dates can change behavior. |
| What task and prompt were used? | Medical triage, summarization and creative writing have different failure modes. |
| Who were the users or evaluators? | Experts may identify problems that a general user misses; user context affects the answer. |
| What counted as an error or harm? | “Problematic,” “unsafe,” incomplete and factually wrong are not interchangeable categories. |
| Were safeguards and human escalation present? | Deployment architecture, filters and review can change the practical risk. |
The HealthAdvice results demonstrate why this context is essential: rates differed across four models under one fixed question set and rating method. They do not prove that the model with the lowest rate is safest for every population or task.
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Evaluation before and after release
Organizations should test realistic, underspecified questions, include vulnerable populations and measure both incorrect and unsafe outputs. Monitoring should continue after release because model updates, new prompts and changing use patterns can introduce failures that an initial test missed.
Technical and access controls
Relevant deployments can combine permission controls, data minimization, local processing where appropriate and validation filters for high-risk outputs. Controls should be tested against prompt injection and unauthorized access rather than assumed to work because they exist.
Human expertise and lived experience
WHO-supported workshop recommendations published in 2026 called for mental-health experts and people with lived experience, including young people, to help design these tools. They also recommended impact assessment that includes mental health, crisis-referral frameworks and clear accountability. More than 30 international experts attended that January 2026 workshop. These recommendations describe safeguards to build; they are not evidence that a particular consumer chatbot is clinically safe.
Governance and transparency
WHO’s governance principles include autonomy, safety, transparency, accountability, inclusion and sustainability. In practice, users need to know what a system is for, what it cannot do, how data is handled, when a human takes over and how to report a harmful response.
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When a chatbot is useful—and when it is the wrong tool
Low-stakes uses can include brainstorming, rephrasing, creating a study outline or generating a list of questions to ask an expert. Even then, factual claims should be checked when accuracy matters.
Do not make the chatbot the sole decision-maker when an error could cause physical injury, delay urgent care, expose confidential information, worsen a mental-health crisis or create substantial legal or financial harm. In those situations, use the chatbot, if at all, only to organize information for a qualified human who can review the underlying facts.
The practical conclusion
AI chatbots are unreliable when their polished language exceeds their evidence, context or safeguards. The most important protections are independent verification, deliberate privacy choices, explicit escalation to qualified people and evaluation that measures real-world unsafe behavior rather than fluency alone. Treating a generated answer as a draft to check—not an authority to obey—reduces the chance that a plausible mistake becomes a harmful decision.
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