Large language model responses about PMOS : information quality, readability, transparency and perceived empathy

BACKGROUND Freely accessible large language models provide health information, but their communication characteristics require evaluation. We compared information quality, transparency, readability and perceived empathy for questions about polyendocrine metabolic ovarian syndrome (PMOS; formerly PCOS). METHODS Thirty-three clinician-formulated English questions using PCOS terminology were submitted once to each of three free-access configurations during 20-25 July 2026. Two clinicians rated DISCERN, EQIP, JAMA benchmarks, global quality and perceived empathy. Six readability indices were calculated. Matched comparisons used Friedman tests, paired signed-rank permutation comparisons and Holm correction. RESULTS Across 99 archived responses, the DeepSeek-V4 configuration received higher final ratings than GPT-5.5 and Gemini 3.5 on all five clinician-rated measures (Holm-adjusted pairwise P < 0.05). Median DISCERN scores were 67.0, 62.0 and 61.0, respectively. DeepSeek-V4 had higher median ARI, GFI, FKGL and SMOG values, whereas GPT-5.5 had the highest median Coleman-Liau score. FRES showed no overall difference (adjusted P = 0.100). Initial empathy agreement was low (ICC 0.05-0.43). CONCLUSION Ratings differed for the specific configurations and single responses examined in this treatment-heavy question set. The findings neither establish stable model rankings nor demonstrate clinical correctness, safety or effectiveness for patients.