trustmebro.to/r/…
✓ checked
trust me, bro:
here is the receipt.
the claim

Large language models used for medical question answering generate hallucinated clinical information.

the verdict
SUPPORTED
the evidence backs this
Recorded sources
7 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that large language models used for medical question answering generate hallucinated clinical information?

Yes. A study measuring hallucination behaviour of LLMs on medical textbook QA directly documents hallucinated clinical information, and several other peer-reviewed studies report inaccurate or unreliable LLM output in medical question answering and education.

Verdict: SUPPORTED · 7 sources for, 0 against · Last checked

The private library was unavailable for some searches in this check. Missing library results are not evidence against the claim.

Evidence for · 7
Causality Extraction from Medical Text Using Large Language Models (LLMs)
2024
Get citation
Causality Extraction from Medical Text Using Large Language Models (LLMs). (2024). doi.org. https://doi.org/10.3390/info16010013

See more details
More for · 6
The Effects of Large Language Models on the Generation of Multiple-choice Questions in Medical Education: A Scoping Review
2026
Get citation
Mohammed, B. E. A., Mohammed, T. E. A., & Breitbach, A. P. (2026). The Effects of Large Language Models on the Generation of Multiple-choice Questions in Medical Education: A Scoping Review. doi.org. https://doi.org/10.18502/sjms.v21i2.19868

Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks
2026
Get citation
Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks. (2026). doi.org. https://doi.org/10.18653/v1/2026.healing-1.13

Multi-Agent Cross-Review of Large Language Models for Medical Question Answering Under Noisy Clinical Text: Development and Evaluation Study (Preprint)
2026
Get citation
Lee, H., Kim, S. J., & Lee, S. (2026). Multi-Agent Cross-Review of Large Language Models for Medical Question Answering Under Noisy Clinical Text: Development and Evaluation Study (Preprint). doi.org. https://doi.org/10.2196/preprints.104714

Addressing Patient Questions on Spinal Muscular Atrophy: Performance of Large Language Models in a Genetic Context
2026
Get citation
Erkan, D. D. & Alikaşifoğlu, M. (2026). Addressing Patient Questions on Spinal Muscular Atrophy: Performance of Large Language Models in a Genetic Context. doi.org. https://doi.org/10.12996/gmj.2026.4626

Quantifying Hallucinations in Language Language Models on Medical Textbooks -
Get citation
Quantifying Hallucinations in Language Language Models on Medical Textbooks - PMC Lock. (n.d.). pmc.ncbi.nlm.nih.gov. https://pmc.ncbi.nlm.nih.gov/articles/PMC13119327/

Evaluating GPT-4 Responses on Scars or Keloids for Patient Education: Large Language Model Evaluation Study.
2026
Get citation
M, R., T, X., & W, H. (2026). Evaluating GPT-4 Responses on Scars or Keloids for Patient Education: Large Language Model Evaluation Study.. doi.org. https://doi.org/10.2196/78838

Receipt History
Anyone with this link can read the claim and its public receipt, including any personal information in that text. Open permanent receipt.
Check your own claim
Challenge the receipt
Requests are recorded for review. This does not start an automatic check or guarantee a response time.

Citation formatting by citeproc-js (Frank Bennett) and the Citation Style Language project. Source and licenses.

This receipt is an automated verdict against our published method · not an opinion about any author or publication.
Terms · Privacy · How verdicts work