BACKGROUND: We queried ChatGPT (OpenAI) and Google Assistant about amblyopia and compared their answers with the keywords found on the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) website, specifically the section on amblyopia. Out of the 26 keywords chosen from the website, ChatGPT included 11 (42%) in its responses, while Google included 8 (31%).
OBJECTIVE: Our study investigated the adherence of ChatGPT-3.5 and Google Assistant to the guidelines of the AAPOS for patient education on amblyopia.
METHODS: ChatGPT-3.5 was used. The four questions taken from the AAPOS website, specifically its glossary section for amblyopia, are as follows: (1) What is amblyopia? (2) What causes amblyopia? (3) How is amblyopia treated? (4) What happens if amblyopia is untreated? Approved and selected by ophthalmologists (GW and DL), the keywords from AAPOS were words or phrases that deemed significant for the education of patients with amblyopia. The \"Flesch-Kincaid Grade Level\" formula, approved by the US Department of Education, was used to evaluate the reading comprehension level for the responses from ChatGPT, Google Assistant, and AAPOS.
RESULTS: In their responses, ChatGPT did not mention the term \"ophthalmologist,\" whereas Google Assistant and AAPOS both mentioned the term once and twice, respectively. ChatGPT did, however, use the term \"eye doctors\" once. According to the Flesch-Kincaid test, the average reading level of AAPOS was 11.4 (SD 2.1; the lowest level) while that of Google was 13.1 (SD 4.8; the highest required reading level), also showing the greatest variation in grade level in its responses. ChatGPT\'s answers, on average, scored 12.4 (SD 1.1) grade level. They were all similar in terms of difficulty level in reading. For the keywords, out of the 4 responses, ChatGPT used 42% (11/26) of the keywords, whereas Google Assistant used 31% (8/26).
CONCLUSIONS: ChatGPT trains on texts and phrases and generates new sentences, while Google Assistant automatically copies website links. As ophthalmologists, we should consider including \"see an ophthalmologist\" on our websites and journals. While ChatGPT is here to stay, we, as physicians, need to monitor its answers.

Background Millions of individuals every day turn to the internet for assistance in understanding their hand conditions and potential treatments. While online educational resources appear abundant, there are concerns about whether resources meet the readability recommendations agreed upon by the American Medical Association (AMA) and the National Institutes of Health (NIH). Identifying educational resources that are readable for the majority of patients could improve a patient\'s understanding of their medical condition, subsequently improving their health outcomes. Methods The readability of the top five websites for the 10 most common hand conditions was examined using the Flesch-Kincaid (FK) analysis, comprising the FK reading ease and FK grade level. The FK reading ease score is an indicator of how difficult a text is to comprehend, while the FK grade level score is the grade level an individual reading a particular text would need to fully understand the text. Results The average FK reading ease was 56.00, which correlates with \"fairly difficult (high school)\". The average FK corresponded to an eighth-grade reading level, far above the sixth-grade reading level recommendation set by the AMA and NIH. Conclusion Patient education, satisfaction, and the patient-physician relationship can all be improved by providing patients with more readable educational materials. Our study shows there is an opportunity for drastic improvement in the readability of online educational materials. Guiding patients with effective search techniques, advocating for the creation of more readable materials, and having a better understanding of the health literacy barriers patients face will allow hand surgeons to provide more comprehensive care to patients.

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Rampant social media use allows individuals and organizations to broadcast their views to scoping audiences with minimal requirements for vetting or validating shared information. We discuss the impact of disinformation transmitted via social media and use the recent example of false information broadcast concerning sunscreens recently reported in the Wall Street Journal. We also highlight the ethical consequences of social media influencers who disseminate unchecked information and the need for healthcare professionals to be involved to enhance accountability, goodwill, and truthfulness.

BACKGROUND: Hashimoto thyroiditis (HT) is an autoimmune thyroid disease and the leading cause of hypothyroidism in areas with sufficient iodine intake. The quality-of-life impact and financial burden of hypothyroidism and HT highlight the need for additional research investigating the disease etiology with the aim of revealing potential modifiable risk factors.
OBJECTIVE: Implementation of measures against such risk factors, once identified, has the potential to lessen the financial burden while also improving the quality of life of many individuals. Therefore, we aimed to examine the potential seasonality of HT in Europe using the Google Trends data to explore whether there is a seasonal characteristic of Google searches regarding HT, examine the potential impact of the countries\' geographic location on the potential seasonality, and identify potential modifiable risk factors for HT, thereby inspiring future research on the topic.
METHODS: Monthly Google Trends data on the search topic \"Hashimoto thyroiditis\" were retrieved in a 17-year time frame from January 2004 to December 2020 for 36 European countries. A cosinor model analysis was conducted to evaluate potential seasonality. Simple linear regression was used to estimate the potential effect of latitude and longitude on seasonal amplitude and phase of the model outputs.
RESULTS: Of 36 included European countries, significant seasonality was observed in 30 (83%) countries. Most phase peaks occurred in spring (14/30, 46.7%) and winter (8/30, 26.7%). A statistically significant effect was observed regarding the effect of geographical latitude on cosinor model amplitude (y = -3.23 + 0.13 x; R2=0.29; P=.002). Seasonal increases in HT search volume may therefore be a consequence of an increased incidence or higher disease activity. It is particularly interesting that in most countries, a seasonal peak occurred in spring and winter months; when viewed in the context of the statistically significant impact of geographical latitude on seasonality amplitude, this may indicate the potential role of vitamin D levels in the seasonality of HT.
CONCLUSIONS: Significant seasonality of HT Google Trends search volume was observed in our study, with seasonal peaks in most countries occurring in spring and winter and with a significant impact of latitude on seasonality amplitude. Further studies on the topic of seasonality in HT and factors impacting it are required.

UNASSIGNED: Chat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patients can use ChatGPT to better understand medical issues. This study compared the two search engines based on: (i) frequently asked questions (FAQs) about Femoroacetabular Impingement Syndrome (FAI), (ii) the corresponding answers to these FAQs, and (iii) the most FAQs yielding a numerical response.
UNASSIGNED: To assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches.
UNASSIGNED: Cross-sectional study.
UNASSIGNED: The same keywords were used to search the 10 most common questions about FAI on both Google and ChatGPT. The responses from both search engines were recorded and analyzed.
UNASSIGNED: Of the 20 questions, 8 (40%) were similar. Among the 10 questions searched on Google, 7 were provided by a medical practice. For numerical questions, there was a notable difference in answers between Google and ChatGPT for 3 out of the top 5 most common questions (60%). Expert evaluation indicated that 67.5% of experts were satisfied or highly satisfied with the accuracy of ChatGPT\'s descriptions of both conservative and surgical treatment options for FAI. Additionally, 62.5% of experts were satisfied or highly satisfied with the safety of the information provided. Regarding the etiology of FAI, including cam and pincer impingements, 52.5% of experts expressed satisfaction or high satisfaction with ChatGPT\'s explanations. Overall, 62.5% of experts affirmed that ChatGPT could serve effectively as a reliable medical resource for initial information retrieval.
UNASSIGNED: This study confirms that ChatGPT, despite being a new tool, shows significant potential as a supplementary resource for health information on FAI. Expert evaluations commend its capacity to provide accurate and comprehensive responses, valued by medical professionals for relevance and safety. Nonetheless, continuous improvements in its medical content\'s depth and precision are recommended for ongoing reliability. While ChatGPT offers a promising alternative to traditional search engines, meticulous validation is imperative before it can be fully embraced as a trusted medical resource.

BACKGROUND: Telemedicine offers a multitude of potential advantages, such as enhanced health care accessibility, cost reduction, and improved patient outcomes. The significance of telemedicine has been underscored by the COVID-19 pandemic, as it plays a crucial role in maintaining uninterrupted care while minimizing the risk of viral exposure. However, the adoption and implementation of telemedicine have been relatively sluggish in certain areas. Assessing the level of interest in telemedicine can provide valuable insights into areas that require enhancement.
OBJECTIVE: The aim of this study is to provide a comprehensive analysis of the level of public and research interest in telemedicine from 2017 to 2022 and also consider any potential impact of the COVID-19 pandemic.
METHODS: Google Trends data were retrieved using the search topics \"telemedicine\" or \"e-health\" to assess public interest, geographic distribution, and trends through a joinpoint regression analysis. Bibliographic data from Scopus were used to chart publications referencing the terms \"telemedicine\" or \"eHealth\" (in the title, abstract, and keywords) in terms of scientific production, key countries, and prominent keywords, as well as collaboration and co-occurrence networks.
RESULTS: Worldwide, telemedicine generated higher mean public interest (relative search volume=26.3%) compared to eHealth (relative search volume=17.6%). Interest in telemedicine remained stable until January 2020, experienced a sudden surge (monthly percent change=95.7%) peaking in April 2020, followed by a decline (monthly percent change=-22.7%) until August 2020, and then returned to stability. A similar trend was noted in the public interest regarding eHealth. Chile, Australia, Canada, and the United States had the greatest public interest in telemedicine. In these countries, moderate to strong correlations were evident between Google Trends and COVID-19 data (ie, new cases, new deaths, and hospitalized patients). Examining 19,539 original medical articles in the Scopus database unveiled a substantial rise in telemedicine-related publications, showing a total increase of 201.5% from 2017 to 2022 and an average annual growth rate of 24.7%. The most significant surge occurred between 2019 and 2020. Notably, the majority of the publications originated from a single country, with 20.8% involving international coauthorships. As the most productive country, the United States led a cluster that included Canada and Australia as well. European, Asian, and Latin American countries made up the remaining 3 clusters. The co-occurrence network categorized prevalent keywords into 2 clusters, the first cluster primarily focused on applying eHealth, mobile health (mHealth), or digital health to noncommunicable or chronic diseases; the second cluster was centered around the application of telemedicine and telehealth within the context of the COVID-19 pandemic.
CONCLUSIONS: Our analysis of search and bibliographic data over time and across regions allows us to gauge the interest in this topic, offer evidence regarding potential applications, and pinpoint areas for additional research and awareness-raising initiatives.

BACKGROUND: With the rise of machine learning applications in health care, shifts in medical fields that rely on precise prognostic models and pattern detection tools are anticipated in the near future. Chat Generative Pretrained Transformer (ChatGPT) is a recent machine learning innovation known for producing text that mimics human conversation. To gauge ChatGPT\'s capability in addressing patient inquiries, the authors set out to juxtapose it with Google Search, America\'s predominant search engine. Their comparison focused on: 1) the top questions related to clinical practice guidelines from the American Academy of Family Physicians by category and subject; 2) responses to these prevalent questions; and 3) the top questions that elicited a numerical reply.
METHODS: Utilizing a freshly installed Google Chrome browser (version 109.0.5414.119), the authors conducted a Google web search (www.google.com) on March 4, 2023, ensuring minimal influence from personalized search algorithms. Search phrases were derived from the clinical guidelines of the American Academy of Family Physicians. The authors prompted ChatGPT with: \"Search Google using the term \'(refer to search terms)\' and document the top four questions linked to the term.\" The same 25 search terms were employed. The authors cataloged the primary 4 questions and their answers for each term, resulting in 100 questions and answers.
RESULTS: Of the 100 questions, 42% (42 questions) were consistent across all search terms. ChatGPT predominantly sourced from academic (38% vs 15%, p = 0.0002) and government (50% vs 39%, p = 0.12) domains, whereas Google web searches leaned toward commercial sources (32% vs 11%, p = 0.0002). Thirty-nine percent (39 questions) of the questions yielded divergent answers between the 2 platforms. Notably, 16 of the 39 distinct answers from ChatGPT lacked a numerical reply, instead advising a consultation with a medical professional for health guidance.
CONCLUSIONS: Google Search and ChatGPT present varied questions and answers for both broad and specific queries. Both patients and doctors should exercise prudence when considering ChatGPT as a digital health adviser. It\'s essential for medical professionals to assist patients in accurately communicating their online discoveries and ensuing inquiries for a comprehensive discussion.

OBJECTIVE: To evaluate the quality, readability, and accuracy of large language model (LLM)-generated patient education materials (PEMs) on childhood glaucoma, and their ability to improve existing the readability of online information.
METHODS: Cross-sectional comparative study.
METHODS: We evaluated responses of ChatGPT-3.5, ChatGPT-4, and Bard to 3 separate prompts requesting that they write PEMs on \"childhood glaucoma.\" Prompt A required PEMs be \"easily understandable by the average American.\" Prompt B required that PEMs be written \"at a 6th-grade level using Simple Measure of Gobbledygook (SMOG) readability formula.\" We then compared responses\' quality (DISCERN questionnaire, Patient Education Materials Assessment Tool [PEMAT]), readability (SMOG, Flesch-Kincaid Grade Level [FKGL]), and accuracy (Likert Misinformation scale). To assess the improvement of readability for existing online information, Prompt C requested that LLM rewrite 20 resources from a Google search of keyword \"childhood glaucoma\" to the American Medical Association-recommended \"6th-grade level.\" Rewrites were compared on key metrics such as readability, complex words (≥3 syllables), and sentence count.
RESULTS: All 3 LLMs generated PEMs that were of high quality, understandability, and accuracy (DISCERN ≥4, ≥70% PEMAT understandability, Misinformation score = 1). Prompt B responses were more readable than Prompt A responses for all 3 LLM (P ≤ .001). ChatGPT-4 generated the most readable PEMs compared to ChatGPT-3.5 and Bard (P ≤ .001). Although Prompt C responses showed consistent reduction of mean SMOG and FKGL scores, only ChatGPT-4 achieved the specified 6th-grade reading level (4.8 ± 0.8 and 3.7 ± 1.9, respectively).
CONCLUSIONS: LLMs can serve as strong supplemental tools in generating high-quality, accurate, and novel PEMs, and improving the readability of existing PEMs on childhood glaucoma.

Computer vision-based analysis of street view imagery has transformative impacts on environmental assessments. Interactive web services, particularly Google Street View, play an ever-important role in making imagery data ubiquitous. Despite the technical ease of harnessing millions of Google Street View images, this article questions the current practices in using this proprietary data source from a European viewpoint. Our concern lies with Google\'s terms of service, which restrict bulk image downloads and the generation of street view image-based indices. To reconcile the challenge of advancing society through groundbreaking research while maintaining data license agreements and legal integrity, we believe it is crucial to 1) include an author\'s statement on using proprietary street view data and the directives it entails, 2) negotiate academic-specific license to democratize Google Street View data access, and 3) adhere to open data principles and utilize open image sources for future research.