Observational studyMedicine2025
Assessing the quality and readability of patient education materials on chemotherapy cardiotoxicity from artificial intelligence chatbots: An observational cross-sectional study.
Observational study in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence (AI) and Healthcare Capabilities: A Systematic Review and Research Directions.F1000Research · 2025Pooled it
- Flatfoot and artificial intelligence: Can language models be trusted for patient education?Medicine · 2026Article
- An expert-based assessment of the quality, reliability, understandability, and readability of ChatGPT responses on cerebral palsy.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- The Impact of Specific Prompt Engineering Techniques on the Readability of LLM-Generated Patient Materials in Gastroenterology and Hepatology.Digestive diseases and sciences · 2026Article
- AI-generated patient information leaflets for oral anticoagulants: quality, usability, readability, and reproducibility compared with FDA-referenced materials.International journal of clinical pharmacy · 2026Article
- Article
- A Comparison of a Customized Peripheral Artery Disease (PAD)-Specific Generative AI Chatbot and General-Purpose AI Chatbots for PAD Patient Education.Journal of clinical medicine · 2026Article
- AI-generated patient education for ankylosing spondylitis: a comparative study of readability and quality.Clinical rheumatology · 2026Article
- The OB-GYN Take on GPT: Objective Assessment of Artificial Intelligence Models in Patient Education.Cureus · 2026Article
- Information quality, readability, and empathy of AI-generated public mental health information: a comparative evaluation of eight large language models.Frontiers in public health · 2026Article
- Feasibility of a multi-metric framework for evaluating patient-facing AI communication in cosmetic dentistry: an exploratory proof-of-concept study.Frontiers in oral health · 2026Article
- Challenges of using generative artificial intelligence for diabetes patient education: a cross-platform analysis of the quality, readability, and actionability of text generated by large language models.Frontiers in public health · 2026Article
- A centralized decision-making support consultation response for fertility preservation in breast cancer patients: benchmark performance of generative large language models in terms of reliability and readability.Frontiers in public health · 2026Article
- Assessing large language model responses to pediatric depression FAQs: a cross-sectional study on readability, accuracy, and sentiment.Frontiers in psychiatry · 2026Article
- Large Language Models vs. Professional Resources for Post-Treatment Quality-of-Life Questions in Head and Neck Cancer: A Cross-Sectional Comparison.Current oncology (Toronto, Ont.) · 2025Observational
- Readability of Chatbot Responses in Prostate Cancer and Urological Care: Objective Metrics Versus Patient Perceptions.Current oncology (Toronto, Ont.) · 2025Article
- Quality evaluation of AI-generated diabetes-related health education texts from different generative models.Digital healthArticle
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and the introduction of Large Language Model (LLM) chatbots have become a common source of patient inquiry in healthcare. The quality and readability of AI-generated patient education materials (PEM) is the subject of many studies across multiple medical topics. Most demonstrate poor readability and acceptable quality. However, an area yet to be investigated is chemotherapy-induced cardiotoxicity. This study seeks to assess the quality and readability of chatbot created PEM relative to chemotherapy-induced cardiotoxicity. We conducted an observational cross-sectional study in August 2024 by asking 10 questions to 4 chatbots: ChatGPT, Microsoft Copilot (Copilot), Google Gemini (Gemini), and Meta AI (Meta). The generated material was assessed for readability using 7 tools: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. Quality was assessed using modified versions of 2 validated tools: the Patient Education Materials Assessment Tool (PEMAT), which outputs a 0% to 100% score, and DISCERN, a 1 (unsatisfactory) to 5 (highly satisfactory) scoring system. Descriptive statistics were used to evaluate performance and compare chatbots amongst each other. Mean reading grade level (RGL) across all chatbots was 13.7. Calculated RGLs for ChatGPT, Copilot, Gemini and Meta were 14.2, 14.0, 12.5, 14.2, respectively. Mean DISCERN scores across the chatbots was 4.2. DISCERN scores for ChatGPT, Copilot, Gemini, and Meta were 4.2, 4.3, 4.2, and 3.9, respectively. Median PEMAT scores for understandability and actionability were 91.7% and 75%, respectively. Understandability and actionability scores for ChatGPT, Copilot, Gemini, and Meta were 100% and 75%, 91.7% and 75%, 90.9% and 75%, and 91.7% and 50%, respectively. AI chatbots produce high quality PEM with poor readability. We do not discourage using chatbots to create PEM but recommend cautioning patients about their readability concerns. AI chatbots are not an alternative to a healthcare provider. Furthermore, there is no consensus on which chatbots create the highest quality PEM. Future studies are needed to assess the effectiveness of AI chatbots in providing PEM to patients and how the capabilities of AI chatbots are changing over time.
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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.