ArticleCardiology and therapy2024
Can Artificial Intelligence Improve the Readability of Patient Education Materials on Aortic Stenosis? A Pilot Study.
Article in Cardiology and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.
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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
56 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Large Language Models in Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- Artificial intelligence technology in aortic valve disease: a decade of scientometric and narrative review.Frontiers in cardiovascular medicine · 2026Pooled it
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Large Language Model-Based Simplification of Digital Therapeutics Explanations for Insomnia and Nicotine Dependence: Two Randomized Online Experiments.JMIR human factors · 2026Trial
- A Comparative Evaluation of ChatGPT and Google Search Output for Caregiver Education in Tetralogy of Fallot.Cardiology and therapy · 2026Article
- Understandability and Actionability of Artificial Intelligence-Assisted Lymphedema Education Material in Patients Undergoing Breast Cancer Surgery: Expert Evaluation.Journal of clinical nursing · 2026Observational
- Concordance of Large Language Model Recommendations with Multidisciplinary Heart Team Decisions in Coronary Revascularization and Aortic Valve Intervention: A Systematic Review and Pooled Analysis.Cardiology and therapy · 2026Review
- Understanding the small print: a 5-year review of readability in phase I trial information leaflets.Trials · 2026Article
- Cloudy or Clear? Readability and Content Analysis of Patient Education Materials in Myasthenia Gravis.Muscle & nerve · 2026Article
- Performance of ChatGPT-4o in Providing Information on Pediatric Inborn Errors of Immunity: A Cross-Sectional Evaluation.Journal of clinical medicine · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Readability of Online Patient Education Materials for Myopia Management.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Gonarthrosis Advisor vs ChatGPT-5: quality and readability of artificial intelligence-generated patient education for knee osteoarthritis.Acta orthopaedica et traumatologica turcica · 2026Article
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Can Large Artificial Intelligence-Based Linguistic Models Help to Obtain Information About Burning Mouth Syndrome?Oral diseases · 2026Article
- Bridging the Gap: A Pilot Study Using Artificial Intelligence to Make Plastic Surgery Research Accessible.Plastic and reconstructive surgery. Global open · 2026Article
- Utility of ChatGPT-4o in Creating Patient Handouts in Ophthalmology: A Comparison With American Academy of Ophthalmology Educational Materials.Translational vision science & technology · 2026Article
- Leveraging Large Language Models to Improve the Readability of German Online Medical Texts: Evaluation Study.JMIR AI · 2026Article
- Artificial intelligence as a source of psychotherapy-related psychoeducation: a cross-sectional content analysis of information on eye movement desensitization and reprocessing.Frontiers in psychiatry · 2026Article
- Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe advent of generative artificial intelligence (AI) dialogue platforms and large language models (LLMs) may help facilitate ongoing efforts to improve health literacy. Additionally, recent studies have highlighted inadequate health literacy among patients with cardiac disease. The aim of the present study was to ascertain whether two freely available generative AI dialogue platforms could rewrite online aortic stenosis (AS) patient education materials (PEMs) to meet recommended reading skill levels for the public.
methodsOnline PEMs were gathered from a professional cardiothoracic surgical society and academic institutions in the USA. PEMs were then inputted into two AI-powered LLMs, ChatGPT-3.5 and Bard, with the prompt "translate to 5th-grade reading level". Readability of PEMs before and after AI conversion was measured using the validated Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook Index (SMOGI), and Gunning-Fog Index (GFI) scores.
resultsOverall, 21 PEMs on AS were gathered. Original readability measures indicated difficult readability at the 10th-12th grade reading level. ChatGPT-3.5 successfully improved readability across all four measures (p < 0.001) to the approximately 6th-7th grade reading level. Bard successfully improved readability across all measures (p < 0.001) except for SMOGI (p = 0.729) to the approximately 8th-9th grade level. Neither platform generated PEMs written below the recommended 6th-grade reading level. ChatGPT-3.5 demonstrated significantly more favorable post-conversion readability scores, percentage change in readability scores, and conversion time compared to Bard (all p < 0.001).
conclusionAI dialogue platforms can enhance the readability of PEMs for patients with AS but may not fully meet recommended reading skill levels, highlighting potential tools to help strengthen cardiac health literacy in the future.
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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.