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Aim And Background: Patients are increasingly turning to the internet to learn more about their ocular disease. In this study, we sought (1) to compare the accuracy and readability of Google and ChatGPT responses to patients' glaucoma-related frequently asked questions (FAQs) and (2) to evaluate ChatGPT's capacity to improve glaucoma patient education materials by accurately reducing the grade level at which they are written.
Materials And Methods: We executed a Google search to identify the three most common FAQs related to 10 search terms associated with glaucoma diagnosis and treatment. Each of the 30 FAQs was inputted into both Google and ChatGPT and responses were recorded. The accuracy of responses was evaluated by three glaucoma specialists while readability was assessed using five validated readability indices. Subsequently, ChatGPT was instructed to generate patient education materials at specific reading levels to explain seven glaucoma procedures. The accuracy and readability of procedural explanations were measured.
Results: ChatGPT responses to glaucoma FAQs were significantly more accurate than Google responses (97 vs 77% accuracy, respectively, < 0.001). ChatGPT responses were also written at a significantly higher reading level (grade 14.3 vs 9.4, respectively, < 0.001). When instructed to revise glaucoma procedural explanations to improve understandability, ChatGPT reduced the average reading level of educational materials from grade 16.6 (college level) to grade 9.4 (high school level) ( < 0.001) without reducing the accuracy of procedural explanations.
Conclusion: ChatGPT is more accurate than Google search when responding to glaucoma patient FAQs. ChatGPT successfully reduced the reading level of glaucoma procedural explanations without sacrificing accuracy, with implications for the future of customized patient education for patients with varying health literacy.
Clinical Significance: Our study demonstrates the utility of ChatGPT for patients seeking information about glaucoma and for physicians when creating unique patient education materials at reading levels that optimize understanding by patients. An enhanced patient understanding of glaucoma may lead to informed decision-making and improve treatment compliance.
How To Cite This Article: Cohen SA, Fisher AC, Xu BY, Comparing the Accuracy and Readability of Glaucoma-related Question Responses and Educational Materials by Google and ChatGPT. J Curr Glaucoma Pract 2024;18(3):110-116.
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http://dx.doi.org/10.5005/jp-journals-10078-1448 | DOI Listing |
Int J Cardiovasc Imaging
September 2025
Klinikum Fürth, Friedrich-Alexander-University Erlangen- Nürnberg, Fürth, Germany.
Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance.
View Article and Find Full Text PDFActa Neurochir (Wien)
September 2025
Department of Neurosurgery, Istinye University, Istanbul, Turkey.
Background: Recent studies suggest that large language models (LLMs) such as ChatGPT are useful tools for medical students or residents when preparing for examinations. These studies, especially those conducted with multiple-choice questions, emphasize that the level of knowledge and response consistency of the LLMs are generally acceptable; however, further optimization is needed in areas such as case discussion, interpretation, and language proficiency. Therefore, this study aimed to evaluate the performance of six distinct LLMs for Turkish and English neurosurgery multiple-choice questions and assess their accuracy and consistency in a specialized medical context.
View Article and Find Full Text PDFJ Glaucoma
September 2025
Harvard Medical School, Boston, MA.
Purpose: Large language models (LLMs) can assist patients who seek medical knowledge online to guide their own glaucoma care. Understanding the differences in LLM performance on glaucoma-related questions can inform patients about the best resources to obtain relevant information.
Methods: This cross-sectional study evaluated the accuracy, comprehensiveness, quality, and readability of LLM-generated responses to glaucoma inquiries.
Skeletal Radiol
September 2025
Department of Orthopaedic Surgery, Northwestern University, Chicago, IL, USA.
Objective: To assess the ability of large language models (LLMs) to accurately simplify lumbar spine magnetic resonance imaging (MRI) reports.
Materials And Methods: Patients who underwent lumbar decompression and/or fusion surgery in 2022 at one tertiary academic medical center were queried using appropriate CPT codes. We then identified all patients with a preoperative ICD diagnosis of lumbar spondylolisthesis and extracted the latest preoperative spine MRI radiology report text.
AJOG Glob Rep
August 2025
Department of Obstetrics, Gynecology & Women's Health, University of Hawaii, Honolulu, HI (Kho).
Background: Within public online forums, patients often seek reassurance and guidance from the community regarding postoperative symptoms and expectations, and when to seek medical assistance. Others are using artificial intelligence in the form of online search engines or chatbots such as ChatGPT or Perplexity. Artificial intelligence chatbot assistants have been growing in popularity; however, clinicians may be hesitant to use them because of concerns about accuracy.
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