Large language models prioritize helpfulness over accuracy in medical contexts.

Question

This claim is accurate. The article correctly cites data from a recent medical paper that examined how language models would respond to illogical medical questions. Researchers found that the models would often times provide inaccurate responses instead of refusing to give inaccurate medical advice. The likelihood of these responses dropped when researchers explicitly instructed the models to avoid illogical answers. 

Chen, Shan et al. “When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior.” NPJ digital medicine vol. 8,1 605. 17 Oct. 2025, doi:10.1038/s41746-025-02008-z

Answer 1

The claim that LLMs provide helpfulness over medical accuracy seems to be largely correct. In a study conducted to test how accurate AI's would be in rejecting requests for misinformation, they found that the AI's would be overly sycophantic and would provide misinformation upwards of 90% of the time. However, they also found that by directly telling the AI's to reject illogical requests that would give misinformation, the rate of giving false information dropped and a reason for why it was rejected was given in about 94% of cases. This suggests that if one is to use an LLM for medical advise, the odds that it will give misinformation is high for illogical requests, but if asked to give only accurate information, it does a much better job of giving the right kind of information or advise.

https://medicalxpress.com/news/2025-10-large-language-prioritize-accuracy-medical.html