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This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p < 0.001). Interestingly, Bing's responses in Nigeria consistently outperformed others (p < 0.05), excelling in all evaluation criteria (p < 0.001). Bard also performed better in Nigeria compared to other regions (p < 0.05), consistently surpassing them across all categories (p < 0.001, with relevance reaching p < 0.01). Notably, Bard's overall scores were significantly higher than those of ChatGPT-3.5 and Bing in all locations (p < 0.001). These findings highlight disparities and opportunities in the quality of AI-powered healthcare information based on user location and platform. This emphasizes the necessity for more research and development to guarantee equal access to trustworthy medical information through AI technologies., (© 2024. The Author(s).)

Background: The readability of online bariatric surgery patient education materials (PEMs) often surpasses the recommended 6th grade level. Large language models (LLMs), like ChatGPT and Bard, have the potential to revolutionize PEM delivery. We aimed to evaluate the readability of PEMs produced by U.S. medical institutions compared to LLMs, as well as the ability of LLMs to simplify their responses., Methods: Responses to frequently asked questions (FAQs) related to bariatric surgery were gathered from top-ranked health institutions. FAQ responses were also generated from GPT-3.5, GPT-4, and Bard. LLMs were then prompted to improve the readability of their initial responses. The readability of institutional responses, initial LLM responses, and simplified LLM responses were graded using validated readability formulas. Accuracy and comprehensiveness of initial and simplified LLM responses were also compared., Results: Responses to 66 FAQs were included. All institutional and initial LLM responses had poor readability, with average reading levels ranging from 9th grade to college graduate. Simplified responses from LLMs had significantly improved readability, with reading levels ranging from 6th grade to college freshman. When comparing simplified LLM responses, GPT-4 responses demonstrated the highest readability, with reading levels ranging from 6th to 9th grade. Accuracy was similar between initial and simplified responses from all LLMs. Comprehensiveness was similar between initial and simplified responses from GPT-3.5 and GPT-4. However, 34.8% of Bard's simplified responses were graded as less comprehensive compared to initial., Conclusion: Our study highlights the efficacy of LLMs in enhancing the readability of bariatric surgery PEMs. GPT-4 outperformed other models, generating simplified PEMs from 6th to 9th grade reading levels. Unlike GPT-3.5 and GPT-4, Bard's simplified responses were graded as less comprehensive. We advocate for future studies examining the potential role of LLMs as dynamic and personalized sources of PEMs for diverse patient populations of all literacy levels., (© 2024. The Author(s).)

Objective: To assess large language models in generating readable uveitis information and in improving the readability of online health information., Design: Evaluation of technology., Subjects: Not applicable., Methods: ChatGPT and Bard were asked the following prompts: (prompt A) "considering that the average American reads at a 6th grade level, using the Flesch-Kincaid Grade Level (FKGL) formula, can you write patient-targeted health information on uveitis of around 6th grade level?" and (prompt B) "can you write patient-targeted health information on uveitis that is easy to understand by an average American?" Additionally, ChatGPT and Bard were asked the following prompt from the first-page results of Google when the term "uveitis" was searched: "Considering that the average American reads at a 6th grade level, using the FKGL formula, can you rewrite the following text to 6th grade level: [insert text]." The readability of each response was analyzed and compared using several metrics described below., Main Outcome Measures: The FKGL is a highly validated readability assessment tool that assigns a grade level to a given text, the total number of words, sentences, syllables, and complex words. Complex words were defined as those with > 2 syllables., Results: ChatGPT and Bard generated responses with lower FKGL scores (i.e., easier to understand) in response to prompt A compared with prompt B. This was only significant for ChatGPT (P < 0.0001). The mean FKGL of responses to ChatGPT (6.3 ± 1.2) was significantly lower (P < 0.0001) than Bard 10.5 ± 0.8. ChatGPT responses also contained less complex words than Bard (P < 0.0001). Online health information on uveitis had a mean grade level of 11.0 ± 1.4. ChatGPT lowered the FKGL to 8.0 ± 1.0 (P < 0.0001) when asked to rewrite the content. Bard was not able to do so (mean FKGL of 11.1 ± 1.6)., Conclusions: ChatGPT can aid clinicians in producing easier-to-understand health information on uveitis for patients compared with already-existing content. It can also help with reducing the difficulty of the language used for uveitis health information targeted for patients., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2023 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

In 2008, the National Heart, Lung, and Blood Institute announced its intent to support a new asthma network known as AsthmaNet. This clinical trials consortium, now in its fifth year, has been charged with developing and executing clinical trials to address the most important asthma management questions and identify new treatment approaches in pediatric and adult patients. This review will discuss the organization of AsthmaNet and the scientific context in which the network was developed and began its work, report the results of an internal priority-setting exercise designed to guide the network's scientific strategy, and highlight the portfolio of clinical trials, proof-of-concept studies, and mechanistic studies planned for the 7-year period of the network to update the global asthma community regarding the progress and processes of the network., (Copyright © 2013 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.)

Clinical asthma studies across different age groups (ie, cross-age studies) can potentially offer insight into the similarities, differences, and relationships between childhood and adult asthma. The National Institutes of Health's Asthma Research Network (AsthmaNet) is unique and innovative in that it has merged pediatric and adult asthma research into a single clinical research network. This combination enhances scientific exchange between pediatric and adult asthma investigators and encourages the application of cross-age studies that involve participants from multiple age groups who are generally not studied together. The experience from AsthmaNet in the development of cross-age protocols highlights some of the issues in the evaluation of cross-age research in asthma. The aim of this review is to summarize these challenges, including the selection of parallel cross-age clinical interventions, identification of appropriate controls, measurement of meaningful clinical outcomes, and various ethical and logistic issues., (Copyright © 2013 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.)