ArticleJournal of medical systems2025
AI Chatbots as Sources of STD Information: A Study on Reliability and Readability.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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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.
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.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- Quality and Readability of Four AI Chatbots Answering Search-Derived Patient Questions About Primary Aldosteronism: A Cross-Sectional Comparative Study.Healthcare (Basel, Switzerland) · 2026Article
- Mapping the Reliability-Readability Gap in the Education of Patients With Age-Related Macular Degeneration Across 6 Large Language Models: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study.JMIR medical informatics · 2026Article
- Article
- Evaluation of Arabic-Language AI Chatbot Responses to Migraine-Related Questions: A Comparative Cross-Sectional Study.Journal of clinical medicine · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Reliability and readability of five AI chatbots for concussion health advice across retrieval augmented and pretrained models.Scientific reports · 2026Article
- Psychotherapists' Trust, Distrust, and Generative AI Practices in Psychotherapy: Qualitative Study.Journal of medical Internet research · 2026Article
- Large Language Models in Infectious Diseases: A Systemic Review.Research square · 2026Article
- Readability and quality assessment of AI-powered chatbot responses to overactive bladder patient questions: a comparative study.BMC urology · 2026Article
- Evaluating search-enabled large language model interfaces for mpox public health consultation: a guideline-based comparative study.Frontiers in public health · 2026Article
- Evaluating the accuracy, reliability, and readability of AI chatbots in delivering postpartum depression information.Frontiers in psychiatry · 2026Article
- Can large language models be trusted? Reliability and readability of responses to perinatal depression FAQs.Frontiers in public health · 2026Article
- Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability.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
- Artificial Intelligence Chatbots as Sources of Cancer Pain Information: A Comparative Evaluation of Quality, Transparency, and Readability.Journal of pain research · 2026Article
- Comment on 'AI Chatbots as Sources of STD Information: A Study on Reliability and Readability'.Journal of medical systems · 2025Article
- Comparative Evaluation of Responses from ChatGPT-5, Gemini 2.5 Flash, Grok 4, and Claude Sonnet-4 Chatbots to Questions About Endodontic Iatrogenic Events.Healthcare (Basel, Switzerland) · 2025Article
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) chatbots are increasingly used for medical inquiries, including sensitive topics like sexually transmitted diseases (STDs). However, concerns remain regarding the reliability and readability of the information they provide. This study aimed to assess the reliability and readability of AI chatbots in providing information on STDs. The key objectives were to determine (1) the reliability of STD-related information provided by AI chatbots, and (2) whether the readability of this information meets the recommended standarts for patient education materials.
methodsEleven relevant STD-related search queries were identified using Google Trends and entered into four AI chatbots: ChatGPT, Gemini, Perplexity, and Copilot. The reliability of the responses was evaluated using established tools, including DISCERN, EQIP, JAMA, and GQS. Readability was assessed using six widely recognized metrics, such as the Flesch-Kincaid Grade Level and the Gunning Fog Index. The performance of chatbots was statistically compared in terms of reliability and readability.
resultsThe analysis revealed significant differences in reliability across the AI chatbots. Perplexity and Copilot consistently outperformed ChatGPT and Gemini in DISCERN and EQIP scores, suggesting that these two chatbots provided more reliable information. However, results showed that none of the chatbots achieved the 6th-grade readability standard. All the chatbots generated information that was too complex for the general public, especially for individuals with lower health literacy levels.
conclusionWhile Perplexity and Copilot showed better reliability in providing STD-related information, none of the chatbots met the recommended readability benchmarks. These findings highlight the need for future improvements in both the accuracy and accessibility of AI-generated health information, ensuring it can be easily understood by a broader audience.
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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.