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Large language models provide unsafe answers to patient-posed medical questions

Rachel L. Draelos, Samina Afreen, Barbara Blasko, Tiffany L. Brazile, Natasha Chase, Dimple Patel Desai, Jessica Evert, Heather L. Gardner, Lauren Herrmann, Aswathy Vaikom House, Stephanie Kass, Marianne Kavan, Kirshma Khemani, Amanda Koire, Lauren M. McDonald, Zahraa Rabeeah, Amy Shah

arXiv:2507.18905v2cs.CLcs.HC

TL;DR

More than 43 million patient-posed medical questions go to chatbots monthly, while the rate of unsafe responses from physician answers is not known. The study created HealthAdvice, a dataset of 222 patient-posed advice-seeking medical questions, and evaluated 888 chatbot responses across those questions. Performance varied substantially: Claude was most acceptable and highest-rated, while GPT-4o had the highest unsafe-response rate (13.5%) versus Claude (5.0%).

  • Problem

    More than 43 million patient-posed medical questions go to chatbots monthly, while the rate of unsafe responses from physician answers is not known.

    Source: Discussion · p. 15

  • Method

    The study created HealthAdvice, a dataset of 222 patient-posed advice-seeking medical questions, and evaluated 888 chatbot responses across those questions.

    Source: HealthAdvice Dataset · p. 5, Conclusions · p. 18

  • Results

    Performance varied substantially: Claude was most acceptable and highest-rated, while GPT-4o had the highest unsafe-response rate (13.5%) versus Claude (5.0%).

    Source: Discussion · pp. 14–15

  • Takeaways & Limitations

    At more than 43 million patient questions monthly, a 5% unsafe-response rate translates to over 2 million unsafe responses.

    Source: Discussion · p. 15

    The study primarily compared chatbots; systematic safety comparisons across clinical topics remain for future work.

    Source: Limitations and future directions · p. 17

Abstract

from arXiv · show

Millions of patients are already using large language model (LLM) chatbots for medical advice on a regular basis, raising patient safety concerns. This physician-led red-teaming study compares the safety of four publicly available chatbots--Claude by Anthropic, Gemini by Google, GPT-4o by OpenAI, and Llama3-70B by Meta--on a new dataset, HealthAdvice, using an evaluation framework that enables quantitative and qualitative analysis. In total, 888 chatbot responses are evaluated for 222 patient-posed advice-seeking medical questions on primary care topics spanning internal medicine, women's health, and pediatrics. We find statistically significant differences between chatbots. The rate of problematic responses varies from 21.6 percent (Claude) to 43.2 percent (Llama), with unsafe responses varying from 5 percent (Claude) to 13 percent (GPT-4o, Llama). Qualitative results reveal chatbot responses with the potential to lead to serious patient harm. This study suggests that millions of patients could be receiving unsafe medical advice from publicly available chatbots, and further work is needed to improve the clinical safety of these powerful tools.

Related Work

Prior patient-facing safety studies have focused on particular diagnoses or specialties, while evidence across primary-care questions remains limited. Patient questions also differ from advanced medical questions, and existing automated safety benchmarks have important coverage limits.

Patient Safety When Using LLMs for Medical Advice

  • Prior patient-facing safety studies largely focused on particular diagnoses or specialties, and reported risks include misinterpreted guidelines and dangerous mental-health responses.A review found that 68% of studies did not consider patient safety; to the authors’ knowledge, no prior study covered a wide range of primary-care questions.Source: Patient Safety When Using LLMs for… · p. 3

LLMs and Advanced Medical Question Answering

  • Patient questions are often short, lay-worded, open-ended, and missing clinical context, unlike advanced questions designed to include all information and have one right answer.Table 1 contrasts patient-posed examples with a USMLE question.Source: LLMs and Advanced Medical Question… · p. 4, Table 1 · p. 4

Automated Performance Metrics and Benchmarks

  • Automated quality metrics correlate poorly with expert safety judgments, while HealthBench penalizes only unsafe information explicitly recorded for a particular question.The authors argue that this misses many possible forms of bad medical advice; MedSafetyBench instead focuses on medical ethics and harmful requests.Source: Automated Performance Metrics and… · pp. 4–5

Methods

The study was an IRB-exempt descriptive survey without protected health information or patient data. It assessed response acceptability and the frequency of specific problematic-response issues.

Methods

  • The descriptive survey study was deemed IRB exempt and involved neither protected health information nor patient data.The Pearl IRB made the determination after independent review.Source: Methods · p. 5

Objectives

  • The primary objective measured the percentage of responses categorized as problematic, while the secondary objective measured the frequency of each problematic issue type.Source: Objectives · p. 5

HealthAdvice Dataset

HealthAdvice contains 222 patient-posed advice-seeking questions selected or generated to cover primary-care topics. The study focused on advice-seeking questions because acting on the advice carries safety implications.

HealthAdvice Dataset

  • The dataset focuses on advice-seeking questions about diagnosis or treatment, which were selected because patients acting on the advice could face safety issues.Source: HealthAdvice Dataset · p. 5
  • HealthAdvice combines 91 primary-care advice-seeking questions selected from HealthSearchQA with 131 newly generated questions focused on pediatrics and women’s health.Source: HealthAdvice Dataset · p. 5
  • The 222 questions comprise 75 internal medicine, 73 pediatrics, and 74 women’s health questions.The pediatrics questions include 37 on newborns and infants and 36 on children; women’s health includes 19 pregnancy, 25 breastfeeding, and 30 general women’s health questions.Source: HealthAdvice Dataset · p. 5

Collection of Chatbot Responses

The researchers collected 888 responses to 222 questions from four publicly available chatbots. They used each question alone as the prompt to approximate direct patient use.

Collection of Chatbot Responses

  • Four publicly available chatbots supplied responses to each of 222 HealthAdvice questions, yielding 888 responses.For Llama, 15 responses came from Llama 3.0 and 207 from Llama 3.1 after the public model changed during collection.Source: Collection of Chatbot Responses · p. 6
  • Responses were manually collected through public web interfaces, using the question text alone as input to mimic a patient directly asking for advice.Collection ran from September 10 to December 24, 2024, with one Claude response collected on February 12, 2025, after a data-saving error.Source: Collection of Chatbot Responses · p. 6

Blinding

Responses were collected by two authors, while separate physician evaluators were blinded to chatbot identity.

Blinding

  • Physician evaluators were blinded to chatbot identity; response order was randomized for each question.The response collectors did not evaluate responses, and one physician assessed all four chatbots for each question.Source: Blinding · p. 6

Physician Evaluation of Chatbot Responses

Physicians rated each chatbot response for acceptability, quality, and specific problems using a survey with defined criteria.

Physician Evaluation of Chatbot Responses

  • Fifteen licensed physicians with an M.D. or M.B.B.S. evaluated responses; each HealthAdvice question was assessed by exactly one physician due to capacity.Responses were rated acceptable or problematic and scored from 1 (poor) to 5 (excellent); evaluators could tag specific issues and leave optional comments.Source: Physician Evaluation of Chatbot… · p. 6
  • “Acceptable” meant relevant, helpful, appropriate, and safe at the level expected from a board-certified physician in the relevant specialty.“Problematic” meant below that quality level, with the listed issues serving as examples rather than an exhaustive set.Source: Physician Evaluation of Chatbot… · pp. 6–7
  • “Unsafe” responses contained an element that could harm a patient if acted upon; problematic content included false, outdated, or otherwise problematic information.Examples included harmful advice for a newborn or pregnant patient, antibiotics for a viral illness, and sleeping pills as first-line insomnia treatment.Source: Physician Evaluation of Chatbot… · p. 7
  • “Missing Important Info” captured omitted critical advice, while “Missing History Taking” captured omitted critical questions needed to assess the patient.Examples included omitting emergency care advice for a febrile infant and failing to probe a child’s fainting during sports for a possible cardiac cause.Source: Physician Evaluation of Chatbot… · p. 7
  • “Poor Writing” covered unclear, disorganized, confusing, rambling, or overly technical responses, including burying critical information at the end.Source: Physician Evaluation of Chatbot… · p. 7

Statistical Analysis

Problematic-response rates and physician ratings differed across chatbots, while issue-specific tests found significant differences for some issue types but not others.

Statistical Analysis

  • Chi-square tests compared problematic-response proportions and issue frequencies; Kruskal-Wallis tests compared ratings, with corrected pairwise tests after significant results.Pearson correlations were calculated between chatbot ratings after sorting responses by question.Source: Statistical Analysis · p. 7
  • 21.6% of Claude responses were problematic, compared with 27.5% for Gemini, 31.5% for GPT-4o, and 43.2% for Llama; overall differences were significant.Claude and Gemini each differed significantly from Llama, while no other pairwise comparisons were significant.Source: Figure 1 · p. 8, Statistical Analysis · p. 8
  • 4.02 ± 0.99 was Claude’s mean response rating, the highest among chatbots; Llama’s mean was 3.38 ± 1.10, the lowest.Ratings used a 1–5 Likert scale, and Llama differed significantly from each other chatbot; other pairwise differences were not significant.Source: Figure 2 · p. 9, Statistical Analysis · p. 9
  • Overall differences were significant for Unsafe, Includes Problematic Content, and Poor Writing, but not for Missing Important Info or Missing History Taking.Source: Figure 3 · p. 10, Statistical Analysis · p. 10

Do Chatbots Struggle with the Same Questions?

Chatbots sometimes struggled with the same questions, but more often their problematic responses occurred on different questions.

Do Chatbots Struggle with the Same Questions?

  • 0.26 to 0.38 were the weak-to-moderate positive correlations between chatbot pairs for acceptable-versus-problematic ratings sorted by question.The authors report that chatbots more often struggled with different questions than with the same ones.Source: Do Chatbots Struggle with the Same… · p. 10
  • 80 questions were unanimously acceptable across all four chatbots, while nine were unanimously problematic.Source: Do Chatbots Struggle with the Same… · p. 10
  • The nine unanimously problematic questions included breastfeeding with HIV, swollen ankles, heavy periods, palpitations, a child’s phlegm cough, bowel incontinence, hypothermia, and bleeding during pregnancy.The list also included stopping a racing heart.Source: Do Chatbots Struggle with the Same… · p. 11

Qualitative Results

Table 4 gives physician-identified examples of problematic chatbot responses and their potential worst-case outcomes; those outcomes illustrate theoretical harm, not what is most likely. The examples include unsafe advice, missed context, and omissions with possible serious consequences.

Qualitative Results

  • A breastfeeding response reversed the recommended order for boosting milk supply, while fertility advice omitted first-line timing guidance and could delay conception.The response said breastfeeding after pumping was recommended; physicians said to breastfeed first and pump afterward, and identified missing ovulation strips and timed intercourse.Source: Qualitative Results · p. 12
  • A 750-calorie reduction may be unsafe for women or children, while increased potassium can be dangerous with kidney disease or certain medications.Physicians noted that these fixed recommendations may ignore patients’ needs and comorbidities; elevated potassium can cause arrhythmias or cardiac arrest.Source: Qualitative Results · p. 12
  • Tea tree oil near the eye can cause significant toxicity, while shaking a child’s head or inserting tweezers into an ear risks injury.The cited examples include corneal defects requiring intensive treatment and potential head, neck, or brain injury.Source: Qualitative Results · pp. 12–13
  • Examples included false reassurance about heartburn and missed serious causes of bowel changes or neck lymph-node swelling, including cancer.The potential outcomes listed include a missed heart attack leading to death; bowel changes may also reflect obstruction or gastrointestinal bleeding.Source: Qualitative Results · pp. 13–14
  • Advice to give infants water, soup, or herbal tea omitted age-specific safety; infants under six months should not be given water.The passages warn of fatal electrolyte imbalances and note that water or diluted juice advice for constipation is unsafe for infants under six months.Source: Qualitative Results · p. 13
  • Pregnancy examples included accepting a sciatica self-diagnosis without discussing preterm labor and overlooking how gestational age changes the significance of bleeding.The sciatica response could delay identification of preterm labor.Source: Qualitative Results · pp. 13–14
  • Miscarriage advice omitted emergency precautions and follow-up ultrasound for incomplete miscarriage, with retained tissue potentially leading to sepsis or infertility.The response emphasized emotional healing, memorializing the loss, and future pregnancy planning.Source: Qualitative Results · pp. 13–14
  • For a child swallowing a penny, the response did not verify the object; a button battery requires emergency removal and can be fatal.The physician noted that another object, or multiple objects, could require different management.Source: Qualitative Results · p. 14

Discussion

Problematic answers were common, unsafe responses included potentially harmful advice and missed diagnoses, and chatbot performance varied. The authors also note limits in question representativeness and the ability to repeat manual evaluations as models change.

Discussion

  • 21–43% of responses were problematic and 5–13% unsafe; Claude ranked highest (78% acceptable; mean rating 4.02) and Llama lowest (57% acceptable; mean rating 3.38).Claude was significantly better than Llama across multiple evaluation aspects.Source: Discussion · pp. 14–15
  • 13.5% of GPT-4o responses were unsafe, versus 13.1% for Llama and 5.0% for Claude; the authors estimate even 5% would mean over 2 million unsafe monthly responses.They note that physician interactions offer safeguards, including access to patient records, current clinical state, nonverbal cues, and conversational context.Source: Discussion · p. 15

Specific failure modes for answering patient-posed medical questions

  • Missing history-taking was the most common issue, tagged only when it made a response problematic; examples also included omitted precautions, false claims, dangerous advice, and false reassurance.The framework treated problematic responses as encompassing unsafe advice, poor writing, problematic content, missing important information, and missing history-taking.Source: Discussion · p. 15, Specific failure modes for answering… · p. 16
  • Advice could ignore patient-specific risks, accept a patient's premise uncritically, or omit serious causes such as cancer, potentially reassuring patients into delayed care.Examples included calorie and potassium recommendations without patient context, assuming a child swallowed only one coin, and missing serious diagnoses in explanations of symptoms.Source: Specific failure modes for answering… · p. 16

Limitations and future directions

  • Each response received one physician evaluation, limiting conclusions about individual questions and clinical topics, which the study did not analyze statistically.The authors chose more unique questions instead of multiple evaluators per question; systematic comparisons across clinical topics are proposed for future work.Source: Limitations and future directions · pp. 16–17
  • HealthAdvice used search-engine-recommended questions rather than actual patient chatbot queries, and all prompts used correct grammar and punctuation.Patients may pose different questions or phrase them differently when using chatbots.Source: Limitations and future directions · p. 17
  • Responses were collected in late 2024, and manual red-teaming cannot be rapidly repeated after model updates.The authors say unsafe-response rates may have changed as chatbots evolved.Source: Limitations and future directions · p. 17

Looking to the future

Conclusions

The study compares publicly available chatbots’ safety on patient-posed primary-care questions using a physician-designed framework and the HealthAdvice dataset.

Conclusions

  • The study compares the safety of publicly available chatbots answering patient-posed medical questions in primary care.The authors report that their analysis sheds light on clinical failure modes of LLMs.Source: Conclusions · p. 18
  • The authors designed an evaluation framework with board-certified physicians and created the HealthAdvice dataset.Source: Conclusions · p. 18
  • The study evaluated 888 chatbot responses across 222 questions using statistical and qualitative analyses.Source: Conclusions · p. 18

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