Evidence map›Paper›PMID 41266988›Full record

ArticleBMC infectious diseases2025

Readability and information quality of LLM-Generated HIV content: a methodological content evaluation.

Guihua Chen, Chuan Lin, Jae Heon Kim, Fuxi Du, Zhao Luo, Yu Seob Shin, Xianxin Li

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Guihua Chen *Department of Biomedical Sciences, Institute for Medical Science, Jeonbuk National University Medical School, Jeonju, Jeollabuk-do, Republic of Korea.ORCID http://orcid.org/0009-0007-0142-3565
Chuan Lin *Department of Obstetrics and Gynecology, Beijing Anzhen Nanchong Hospital, Captial Medical University & Nanchong Central Hospital, Nanchong, Sichuan, China.ORCID http://orcid.org/0009-0005-4204-5375
Jae Heon Kim *Department of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Republic of Korea.
Fuxi DuDepartment of Biomedical Sciences, Institute for Medical Science, Jeonbuk National University Medical School, Jeonju, Jeollabuk-do, Republic of Korea.ORCID http://orcid.org/0009-0007-1287-4978
Zhao Luo *Department of Urology, Shenzhen Qianhai Taikang Hospital, Shenzhen, Guangdong, China. luozhao614@163.com.ORCID http://orcid.org/0009-0005-7807-6296
Yu Seob Shin *Department of Urology, Jeonbuk National University Medical School, Jeonju, Jeollabuk- do, Republic of Korea. ball1210@hanmail.net.ORCID http://orcid.org/0000-0002-1126-3821
Xianxin Li *Department of Urology, Shenzhen Qianhai Taikang Hospital, Shenzhen, Guangdong, China. xianxinli@163.com.ORCID http://orcid.org/0000-0002-5690-0150

Funding

Bio & Medical Technology Development Program of the National Research Foundation (NRF) No. RS-2023-00236157
6 · The paper itself

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.

Indexed as

ComprehensionHIV InfectionsLarge Language ModelsHumansDISCERNHIVInformation qualityInter-rater reliability (ICC)Large language models (LLMs)Readability

Identifiers

PMID41266988
PMCPMC12632083

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.