ArticleBMC infectious diseases2025
Readability and information quality of LLM-Generated HIV content: a methodological content evaluation.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Quality, completeness, readability, and response time of AI chatbots in the management of deep caries and pulp exposure: a bilingual comparative study.BMC oral health · 2026Article
- Performance of large language models on the Turkish Pharmacy Specialty Examination: a comparative analysis of accuracy, confidence, and readability.Scientific reports · 2026Article
- How AI responds to common HIV/AIDS questions: ChatGPT versus DeepSeek.Frontiers in public health · 2026Article
- A comparative cross-sectional evaluation of generative AI chatbots for patient-oriented bacterial vaginosis health advice: safety, accuracy, guideline concordance, empathy, and readability.Frontiers in reproductive health · 2026Article
- Benchmarking the readability, quality, and educational suitability of large language models in communicating pertussis.Frontiers in public health · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundLarge language models (LLMs) are increasingly used to draft public-facing HIV materials, but the balance between readability and information quality remains unclear. This study provides a methodological, content-level evaluation of unedited LLM outputs.
methodsFifteen high-frequency, HIV-relevant lay queries were drawn from Google Trends (using “AIDS” only as the entry term for salience) and mapped to MeSH. From March 13–20, 2025, each query was posed to ChatGPT-4, DeepSeek-R1, and Grok-3 across three conversational rounds (15 × 3 × 3 = 135 texts). Readability of raw outputs was computed using ARI, FRE, GFI, FKGL, and TSI. Information quality was rated with DISCERN by two non-specialist clinicians for all texts and by two infectious-disease specialists for a randomized subset (45 texts). Inter-rater reliability used two-way random-effects ICC; group differences used one-way ANOVA with Tukey’s post hoc or Kruskal–Wallis with Dunn’s post hoc, as appropriate.
resultsARI and GFI showed no between-model differences, whereas FRE, FKGL, and TSI indicated that Grok-3 required higher reading levels than ChatGPT-4 and DeepSeek-R1. In non-specialist ratings, Grok-3 scored higher on DISCERN (56.5 ± 7.7) than ChatGPT-4 (36.3 ± 5.2) and DeepSeek-R1 (36.3 ± 4.9) (both p < 0.0001). Among specialists, Grok-3 (71 [69–73]) exceeded ChatGPT-4 (43.5 [38–49], p < 0.0001) and DeepSeek-R1 (54 [49–59], p = 0.0006); ChatGPT-4 vs. DeepSeek-R1 was not significant (p = 0.2055). ICC among non-specialists was excellent for DeepSeek-R1 (0.95) and Grok-3 (0.98) but moderate for ChatGPT-4 (0.58). Among specialists, ICCs were moderate (≈ 0.59–0.72) with wide confidence intervals.
conclusionsA consistent readability–quality tension emerged: outputs rated higher for information quality tended to be harder to read, whereas more readable texts were less complete. These findings suggest that LLMs may be best suited for upstream use as drafting aids, paired with expert review for accuracy, stigma sensitivity, and audience fit, then edited to target literacy bands and localized, rather than as direct-to-patient systems. This methodological study does not assess reach, behavior change, or clinical outcomes; future work should test comprehension and actionability in target populations and explore low-bandwidth, localized deployments to determine whether content improvements translate into meaningful public-health benefits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.