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AI chatbots can produce an answer that is fluent, specific and confident without having verified the facts or understood the situation. They generate likely sequences of words, not guaranteed truth. Incomplete or biased training data, fabricated details, excessive agreement with the user, sensitivity to wording and missing context can all turn a plausible reply into bad advice.
The risk is greatest when someone acts on an answer about health, safety, finances, legal rights or a crisis. A chatbot can be useful for explaining general information, but it should not be treated as a stand-alone diagnosis, treatment decision-maker or emergency service. Verification and appropriate human judgment are essential.
What makes a chatbot answer unreliable?
A large language model is optimized to produce humanlike text. That objective rewards a response that sounds coherent and helpful, not one that has been checked against a primary source or a complete account of the user’s circumstances. The result can be persuasive wording wrapped around an incorrect premise.
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The World Health Organization (WHO) warned on 16 May 2023 that large language models can generate responses that “appear authoritative and plausible” while being “completely incorrect or contain serious errors,” particularly for health questions. A confident tone, neat formatting or a list of citations does not prove that the underlying claim is true. A citation may be irrelevant, outdated or invented.
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Fabrication and over-agreement
Models can fill gaps with made-up names, studies, quotations, statistics or explanations. They can also be sycophantic: instead of challenging a user’s assumption, the model may accept it and construct an answer around it. These failures are especially dangerous when the user is looking for reassurance and the correct response should be uncertainty, a follow-up question or a referral to a qualified person.
Prompt wording changes the result
Small changes in a prompt can change the answer. The physician-led HealthAdvice study reported poorer answers when patient questions contained typos or misspellings. Asking the same question twice is therefore not a reliable accuracy test: a different wording may produce a different error rather than reveal the truth.
Why missing context is a safety problem
Ordinary questions are often short, open-ended and written in lay language. A person may ask, “What should I do if my child has a fever?” without saying the child’s age, temperature, duration of symptoms, medical history, medicines, pregnancy status, allergies or other warning signs. Safe clinical advice may depend on exactly those details.
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A clinician normally asks follow-up questions, examines the patient when necessary and weighs uncertainty before recommending an action. A general chatbot may answer immediately. The HealthAdvice authors describe cases in which advice failed to account for comorbidities, medications, pregnancy risk or uncertainty about what a child had swallowed. The model may also rely on information the user supplied inaccurately or incompletely.
Training data has uneven quality
Medical training material can include internet-scraped misinformation, lay advice, discriminatory content and guidance that is no longer current. Conversely, useful clinical information may not be freely available online. WHO says biased training data can produce misleading or inaccurate content. A model cannot reliably distinguish a well-supported clinical recommendation from a popular but unsafe claim unless the system has strong retrieval, validation and oversight around it.
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Information can be harmful even when it is technically correct
An answer may describe a real treatment or symptom but still be unsafe for a particular person, dose, interaction, age group or stage of pregnancy. The danger comes from applying a general statement to an individual case without the clinical context needed to interpret it.
What a physician-led evaluation found
The 2026 HealthAdvice study tested four specific model versions on 222 patient-posed, advice-seeking questions in internal medicine, pediatrics and women’s health. Sixteen physicians rated 888 responses. Responses were collected mainly through public chatbot interfaces from 10 September to 24 December 2024; one saved response was collected on 12 February 2025.
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| Evaluated model version | Problematic responses | Unsafe responses |
|---|---|---|
| Claude 3.5 Sonnet | 21.6% | 5% |
| Gemini 1.5 Flash | 27.5% | Not stated separately in the reported comparison |
| GPT-4o | 31.5% | 13% |
| Llama 3.0/3.1-70B | 43.2% | 13% |
These percentages are not a universal chatbot error rate. They describe the named versions, prompts, questions, collection dates and physician-rating method used in that study. A current version, a different interface or a different question could perform differently. The comparison does show why claims that one system is simply “safe” or “accurate” need the test conditions attached.
Why structured test scores are not enough
A model can perform well on a structured examination and still fail on an everyday patient question. Exams generally provide defined information and a known target. Real users often provide a few ambiguous words and expect the system to decide what matters. Safe advice may require a history, examination, local clinical guidance and judgment about whether the person needs urgent care.
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The HealthAdvice authors note that the study’s patient questions resemble how people actually ask for help: briefly and without complete context. The study authors also cite an estimate that 43 million patients in the United States ask chatbots medical questions at least once per month. That figure comes from sources referenced by the authors, not from the HealthAdvice study’s own count, so it should not be treated as a direct measurement of all chatbot use.
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What “harmful” can mean beyond a wrong fact
Harm is not limited to an obviously false sentence. A response can delay needed care, encourage a dangerous action, expose private information, reinforce a false belief or create misplaced confidence. The likelihood depends on the user, the setting, the model and the safeguards around it.
Mental-health and psychological risks
A 2026 scoping review groups reported concerns into five broad areas:
- Psychological or psychiatric effects: possible effects on mood, distress, symptoms or crisis situations.
- Relational and behavioral effects: changes in relationships, dependence, isolation or decisions influenced by the system.
- Cognitive and educational effects: impaired judgment, mistaken learning or over-reliance on generated explanations.
- Output-safety failures: responses that validate a dangerous idea, miss a crisis signal or give unsuitable guidance.
- Governance, privacy and structural risks: data exposure, unequal performance, weak accountability and harms arising from how a tool is deployed.
The review explicitly says that many proposed harms are conceptual and that their likelihood and actual impact remain unclear. Naming a risk is not evidence that it occurs at a known rate, nor does it prove that chatbot use caused a particular outcome. That distinction matters when interpreting alarming anecdotes or broad claims about all users.
Privacy and disinformation risks
WHO identifies possible exposure of sensitive data and misuse of large language models to create convincing disinformation. A user may paste symptoms, a child’s details, an address, workplace information or a private conversation into a service without understanding retention, access or training practices. Even an accurate answer can become a privacy problem if the information was not appropriate to share.
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How deployment can introduce new failure modes
Safety depends on the surrounding application, not only on the model. A system connected to internal records, tools or automated actions has a different risk profile from a stand-alone text box.
Threats identified in an NIST prototype
The National Institute of Standards and Technology’s National Cybersecurity Center of Excellence documented an internal chatbot prototype and described prompt injection, hallucinations, data exposure and unauthorized access as threats. Its example mitigations included local deployment, access controls and validation filters. NIST says the report is a point-in-time description of that prototype and is not implementation guidance.
Why controls do not guarantee safe answers
Access restrictions can limit who reaches a system; validation filters can catch some unsafe content; and local deployment can reduce certain data flows. None of these measures proves that every generated answer is correct. Controls need testing, monitoring, incident handling and a clear route to a human who can override the system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge a claim that one chatbot is safer
Do not compare a single percentage or a marketing statement in isolation. Use the following checklist:
- Exact version and date: identify the model, release or interface tested and when the test occurred.
- Task and prompt: check whether the evaluation used medical triage, factual questions, coding, crisis dialogue or another task, and whether prompts were standardized.
- User and context: note the language, region, age group, clinical setting and amount of information supplied.
- Definition of failure: distinguish a minor inaccuracy, a problematic response and an unsafe response.
- Review method: look for qualified human reviewers, sample size, rating rules and whether reviewers were blinded.
- Safeguards and escalation: ask whether retrieval, filters, access controls, monitoring and human review were present.
- Generalizability: treat results as evidence about the tested setup, not a guarantee for every future conversation.
The HealthAdvice comparison illustrates this principle: rates differed across four models under one question set, while the NIST prototype illustrates how architecture and safeguards also shape risk.
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Safer ways to use a chatbot
Before entering a question
- Decide whether the issue is consequential. For symptoms, medication, self-harm, threats, legal rights, financial loss or personal safety, plan to verify with a qualified human or authoritative service.
- Remove identifying details that are not necessary. Do not paste names, addresses, account numbers, medical-record identifiers or private messages unless you understand the service’s data practices and have a legitimate reason.
- Write down the facts that matter, including dates, age range, relevant conditions, medicines and location. Treat the list as preparation for a professional conversation, not as a substitute for one.
While reading the response
- Look for uncertainty, assumptions and missing questions. A response that never asks for information needed to make a safe decision deserves extra skepticism.
- Separate general education from individualized instructions. A symptom list or explanation is not a diagnosis, and a suggested treatment is not automatically appropriate for you.
- Check every consequential claim against an authoritative source or a qualified professional. Follow the source’s current regional guidance, since recommendations and availability can differ.
- Do not keep prompting until the model gives the answer you want. Rewording can change the output without making it more accurate.
When to stop using the chatbot
Seek urgent human help when there may be an emergency, severe or rapidly worsening symptoms, a poisoning or unknown ingestion, a risk of self-harm or harm to another person, or any situation in which delay could matter. A chatbot cannot examine someone, call emergency services reliably or take responsibility for a clinical decision.
What responsible organizations should build
WHO recommends rigorous evaluation and monitoring, access controls and validation where appropriate, and clear evidence of benefit before widespread routine use in healthcare. Its governance principles include autonomy, safety, transparency, accountability, inclusion and sustainability. These principles require documented ownership of decisions: someone must be responsible for correcting an unsafe output and notifying affected users.
Mental-health tools need specialist involvement
At a WHO-supported workshop in January 2026 attended by more than 30 international experts, recommendations included treating generative-AI use as a public mental-health concern, including mental health in impact assessments and ongoing monitoring, and co-designing tools with mental-health professionals and people with lived experience, including young people. The recommendations also called for crisis-referral frameworks and accountability. They are safeguards to pursue, not proof that any particular consumer chatbot is clinically safe.
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Minimum operational practices
- Test representative, ambiguous and adversarial prompts, not only benchmark questions.
- Measure unsafe and misleading outputs separately from minor factual errors.
- Keep model versions, prompts, policies and evaluation dates so results can be reproduced.
- Limit access to sensitive data and apply least-privilege permissions to connected tools.
- Use validation and monitoring that can be audited, with a documented human escalation path.
- Re-test after model, prompt, retrieval or policy changes; a new version is a new system for safety purposes.
- Include affected users and domain experts in design, testing and incident review.
The practical bottom line
AI chatbots give unreliable or harmful answers because they can generate convincing language without verified knowledge, complete context or dependable judgment. Bias and gaps in training data, fabrication, sycophancy, prompt sensitivity, privacy exposure and deployment vulnerabilities compound the problem. Medical evidence shows meaningful failure rates in a specific tested setup, while mental-health research identifies serious categories of concern but does not establish a single population-wide harm rate. Use chatbots for low-stakes orientation, verify consequential claims and keep qualified humans responsible for decisions that affect health, safety or rights.
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