98%
921
2 minutes
20
Objectives: Educating pediatric patients and their caregivers about the disease is crucial for improving treatment adherence, recognizing complications early, and alleviating anxiety. AI tools such as ChatGPT and Google Gemini offer personalized education, benefiting patients and providers, and are increasingly utilized in healthcare. This study aims to compare patient education guides created by ChatGPT and Google Gemini for acute otitis media, pneumonia, and pharyngitis.
Methods: Patient information guides on pediatric diseases generated by ChatGPT and Google Gemini were evaluated by comparing various variables (words, sentences, average words per sentence, average syllables per word, grade level, and ease score) and further assessed for ease using the Flesch-Kincaid calculator, similarity using Quillbot, and reliability using the Modified Discern score. Statistical analysis was done using R v4.3.2.
Results: Both tools' responses were statistically compared. No significant difference was found in word count (ChatGPT: 477.3; Google Gemini: 394.0; p=0.0765) or sentences (ChatGPT: 35.33; Google Gemini: 46.33; p=0.184). Google Gemini scored slightly higher in ease (ChatGPT: 37.79; Google Gemini: 57.10) and grade level (ChatGPT: 11.40; Google Gemini: 7.43), but these were not statistically significant (p>0.05), indicating no clear superiority.
Conclusions For Practice: In a comparison of patient education guides created by both tools for acute otitis media, pneumonia, and pharyngitis, there was no statistically significant difference to determine the superiority of one AI tool over the other. Further studies should comprehensively evaluate various AI tools across a broader range of diseases. It is also important to assess whether AI tools can provide real-time, verifiable content based on the latest medical advancements.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12377924 | PMC |
http://dx.doi.org/10.7759/cureus.88824 | DOI Listing |
Arch Osteoporos
September 2025
Department of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan City, Taiwan.
Unlabelled: The study assesses the performance of AI models in evaluating postmenopausal osteoporosis. We found that ChatGPT-4o produced the most appropriate responses, highlighting the potential of AI to enhance clinical decision-making and improve patient care in osteoporosis management.
Purpose: The rise of artificial intelligence (AI) offers the potential for assisting clinical decisions.
PLOS Digit Health
September 2025
Department of Dermatology, Stanford University, Stanford, California, United States of America.
Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While these models demonstrate race-based and binary gender biases, anti-LGBTQIA+ bias remains understudied despite documented healthcare disparities affecting these populations. In this work, we evaluated the potential of LLMs to propagate anti-LGBTQIA+ medical bias and misinformation.
View Article and Find Full Text PDFDrug Des Devel Ther
September 2025
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand.
Background: Drug-drug interactions (DDIs) are a critical clinical concern, especially when administering multiple medications, including antidotes. Despite their lifesaving potential, antidotes may interact harmfully with other drugs. However, few studies have specifically investigated DDIs involving antidotes.
View Article and Find Full Text PDFCureus
July 2025
Oral and Maxillofacial Radiology, University of Connecticut (UConn) School of Dental Medicine, Farmington, USA.
Background and aim Orthodontic treatment planning is a complex process requiring a detailed understanding of dental, skeletal, and soft tissue relationships. Traditionally, treatment decisions are made through clinical expertise and evidence-based guidelines. However, the recent evolution of AI, particularly large language models (LLMs), has warranted an evaluation of their capabilities in streamlining clinical workflows.
View Article and Find Full Text PDFCureus
July 2025
Department of Ophthalmology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, TUR.
Purpose This study evaluates the performance of ChatGPT and Google Gemini in addressing refractive surgery-related patient questions by analysing the accuracy, completeness, and readability of their responses. Methods A total of 40 refractive surgery-related questions were compiled and categorized into three levels of difficulty: easy, medium, and hard. Responses from ChatGPT and Google Gemini were blinded and evaluated by two experienced ophthalmologists using standardized criteria.
View Article and Find Full Text PDF