UNASSIGNED: Childhood mental health issues concern a large amount of children worldwide and represent a major public health challenge. The lack of knowledge among parents and caregivers in this area hinders effective management. Empowering families enhances their ability to address their children\'s difficulties, boosts health literacy, and promotes positive changes. However, seeking reliable mental health information remains challenging due to fear, stigma, and mistrust of the sources of information.
UNASSIGNED: This study evaluates the acceptance of a website, CléPsy, designed to provide reliable information and practical tools for families concerned about child mental health and parenting.
UNASSIGNED: This study examines user characteristics and assesses ease of use, usefulness, trustworthiness, and attitude toward using the website. Platform users were given access to a self-administered questionnaire by means of mailing lists, social networks, and posters between May and July 2022.
UNASSIGNED: Findings indicate that the wide majority of the 317 responders agreed or somewhat agreed that the website made discussions about mental health easier with professionals (n=264, 83.3%) or with their relatives (n=260, 82.1%). According to the ANOVA, there was a significant effect between educational level and perceived trust (F6=3.03; P=.007) and between frequency of use and perceived usefulness (F2=4.85; P=.008).
UNASSIGNED: The study underlines the importance of user experience and design in web-based health information dissemination and emphasizes the need for accessible and evidence-based information. Although the study has limitations, it provides preliminary support for the acceptability and usefulness of the website. Future efforts should focus on inclusive co-construction with users and addressing the information needs of families from diverse cultural and educational backgrounds.

Purpose: To evaluate the readability, accountability, accessibility, and source of online patient education materials for treatment of age-related macular degeneration (AMD) and to quantify public interest in Syfovre and geographic atrophy after US Food and Drug Administration (FDA) approval. Methods: Websites were classified into 4 categories by information source. Readability was assessed using 5 validated readability indices. Accountability was assessed using 4 benchmarks of the Journal of the American Medical Association (JAMA). Accessibility was evaluated using 3 established criteria. The Google Trends tool was used to evaluate temporal trends in public interest in \"Syfovre\" and \"geographic atrophy\" in the months after FDA approval. Results: Of 100 websites analyzed, 22% were written below the recommended sixth-grade reading level. The mean (±SD) grade level of analyzed articles was 9.76 ± 3.35. Websites averaged 1.40 ± 1.39 (of 4) JAMA accountability metrics. The majority of articles (67%) were from private practice/independent organizations. A significant increase in the public interest in the terms \"Syfovre\" and \"geographic atrophy\" after FDA approval was found with the Google Trends tool (P < .001). Conclusions: Patient education materials related to AMD treatment are often written at inappropriate reading levels and lack established accountability and accessibility metrics. Articles from national organizations ranked highest on accessibility metrics but were less visible on a Google search, suggesting the need for visibility-enhancing measures. Patient education materials related to the term \"Syfovre\" had the highest average reading level and low accountability, suggesting the need to modify resources to best address the needs of an increasingly curious public.

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: Artificial intelligence (AI) chatbots, such as ChatGPT, have made significant progress. These chatbots, particularly popular among health care professionals and patients, are transforming patient education and disease experience with personalized information. Accurate, timely patient education is crucial for informed decision-making, especially regarding prostate-specific antigen screening and treatment options. However, the accuracy and reliability of AI chatbots\' medical information must be rigorously evaluated. Studies testing ChatGPT\'s knowledge of prostate cancer are emerging, but there is a need for ongoing evaluation to ensure the quality and safety of information provided to patients.
OBJECTIVE: This study aims to evaluate the quality, accuracy, and readability of ChatGPT-4\'s responses to common prostate cancer questions posed by patients.
METHODS: Overall, 8 questions were formulated with an inductive approach based on information topics in peer-reviewed literature and Google Trends data. Adapted versions of the Patient Education Materials Assessment Tool for AI (PEMAT-AI), Global Quality Score, and DISCERN-AI tools were used by 4 independent reviewers to assess the quality of the AI responses. The 8 AI outputs were judged by 7 expert urologists, using an assessment framework developed to assess accuracy, safety, appropriateness, actionability, and effectiveness. The AI responses\' readability was assessed using established algorithms (Flesch Reading Ease score, Gunning Fog Index, Flesch-Kincaid Grade Level, The Coleman-Liau Index, and Simple Measure of Gobbledygook [SMOG] Index). A brief tool (Reference Assessment AI [REF-AI]) was developed to analyze the references provided by AI outputs, assessing for reference hallucination, relevance, and quality of references.
RESULTS: The PEMAT-AI understandability score was very good (mean 79.44%, SD 10.44%), the DISCERN-AI rating was scored as \"good\" quality (mean 13.88, SD 0.93), and the Global Quality Score was high (mean 4.46/5, SD 0.50). Natural Language Assessment Tool for AI had pooled mean accuracy of 3.96 (SD 0.91), safety of 4.32 (SD 0.86), appropriateness of 4.45 (SD 0.81), actionability of 4.05 (SD 1.15), and effectiveness of 4.09 (SD 0.98). The readability algorithm consensus was \"difficult to read\" (Flesch Reading Ease score mean 45.97, SD 8.69; Gunning Fog Index mean 14.55, SD 4.79), averaging an 11th-grade reading level, equivalent to 15- to 17-year-olds (Flesch-Kincaid Grade Level mean 12.12, SD 4.34; The Coleman-Liau Index mean 12.75, SD 1.98; SMOG Index mean 11.06, SD 3.20). REF-AI identified 2 reference hallucinations, while the majority (28/30, 93%) of references appropriately supplemented the text. Most references (26/30, 86%) were from reputable government organizations, while a handful were direct citations from scientific literature.
CONCLUSIONS: Our analysis found that ChatGPT-4 provides generally good responses to common prostate cancer queries, making it a potentially valuable tool for patient education in prostate cancer care. Objective quality assessment tools indicated that the natural language processing outputs were generally reliable and appropriate, but there is room for improvement.

UNASSIGNED: The purpose of this study was to compare the knowledge and practices of specialist and experienced nonspecialist physical therapists in performing patient education about physical activity with patients with heart failure (HF).
UNASSIGNED: Responses on a nationwide anonymous online survey were used to compare specialist and experienced nonspecialist physical therapists on knowledge and frequency of providing physical activity related education to patients hospitalized with acutely decompensated HF. Responses to survey items were scored on 5-point scales ranging from \"Strongly agree\" to \"Strongly disagree\" or \"Always\" to \"Never.\" Mann-Whitney U statistics were used to compare specialist and experienced nonspecialist responses and Wilcoxon signed-ranks tests were used to examine the gap between knowledge and practice.
UNASSIGNED: Twenty-seven specialists and 43 experienced nonspecialists completed the survey. Both groups were similar in age, and experience treating patients hospitalized with acutely decompensated HF. Both groups \"strongly agree\" that they had the required knowledge and skills to educate patients with HF on the physical activity topics. However, specialists more often than experienced nonspecialists provided education on topics such as how to monitor vital signs during physical activity (\"most of the time\" vs. \"about half of the time\") that promoted patient confidence and safety during exercise. Specialists demonstrated a smaller gap between knowledge and frequency of providing patient education than experienced nonspecialists on three of the four patient education topics.
UNASSIGNED: Specialist physical therapists treating patients with HF in the inpatient hospital setting provided patient education on physical activity at a level more closely matching their skills and the clinical practice guideline than did experienced nonspecialists. Physical therapy clinical specialists practicing in the inpatient hospital setting may improve patient outcomes and lower costs to the health care system by improving physical activity adherence and thereby may reduce avoidable hospital readmissions.

BACKGROUND: Surgical site infection (SSI) is a common and costly complication in spinal surgery. Identifying risk factors and preventive strategies is crucial for reducing SSIs. GPT-4 has evolved from a simple text-based tool to a sophisticated multimodal data expert, invaluable for clinicians. This study explored GPT-4\'s applications in SSI management across various clinical scenarios.
METHODS: GPT-4 was employed in various clinical scenarios related to SSIs in spinal surgery. Researchers designed specific questions for GPT-4 to generate tailored responses. Six evaluators assessed these responses for logic and accuracy using a 5-point Likert scale. Inter-rater consistency was measured with Fleiss\' kappa, and radar charts visualized GPT-4\'s performance.
RESULTS: The inter-rater consistency, measured by Fleiss\' kappa, ranged from 0.62 to 0.83. The overall average scores for logic and accuracy were 24.27±0.4 and 24.46±0.25 on 5-point Likert scale. Radar charts showed GPT-4\'s consistently high performance across various criteria. GPT-4 demonstrated high proficiency in creating personalized treatment plans tailored to diverse clinical patient records and offered interactive patient education. It significantly improved SSI management strategies, infection prediction models, and identified emerging research trends. However, it had limitations in fine-tuning antibiotic treatments and customizing patient education materials.
CONCLUSIONS: GPT-4 represents a significant advancement in managing SSIs in spinal surgery, promoting patient-centered care and precision medicine. Despite some limitations in antibiotic customization and patient education, GPT-4\'s continuous learning, attention to data privacy and security, collaboration with healthcare professionals, and patient acceptance of AI recommendations suggest its potential to revolutionize SSI management, requiring further development and clinical integration.

UNASSIGNED: Patients access relevant information concerning their orthopaedic surgery resources through multiple information channels before presenting for clinical treatment. Recently, artificial intelligence (AI)-powered chatbots have become another source of information for patients. The currently developed AI chat technology ChatGPT (OpenAI LP) is an application for such purposes and it has been rapidly gaining popularity, including for patient education. This study sought to evaluate whether ChatGPT can correctly answer frequently asked questions (FAQ) regarding periprosthetic joint infection (PJI).
UNASSIGNED: Twelve FAQs about PJI after hip and knee arthroplasty were identified from the websites of fifteen international clinical expert centres. ChatGPT was confronted with these questions and its responses were analysed for their accuracy using an evidence-based approach by a multidisciplinary team. Responses were categorised in four groups: (1) Excellent response that did not require additional improvement; (2) Satisfactory responses that required a small amount of improvement; (3) Satisfactory responses that required moderate improvement; and (4) Unsatisfactory responses that required a large amount of improvement.
UNASSIGNED: From the analysis of the responses given by the chatbot, no reply received an \'unsatisfactory\' rating; one did not require any correction; and the majority of the responses required low (7 out of 12) or moderate (4 out of 12) clarification. Although a few responses required minimal clarification, the chatbot responses were generally unbiased and evidence-based, even when asked controversial questions.
UNASSIGNED: The AI-chatbot ChatGPT was able to effectively answer the FAQs of patients seeking information around PJI diagnosis and treatment. The given information was also written in a manner that can be assumed to be understandable by patients. The chatbot could be a valuable clinical tool for patient education and understanding around PJI treatment in the future. Further studies should evaluate its use and acceptance by patients with PJI.

UNASSIGNED: End-of-life care (EOLC) is a critical aspect of healthcare, yet accessing reliable information remains challenging, particularly in culturally diverse contexts like India.
UNASSIGNED: This study investigates the potential of artificial intelligence (AI) in addressing the informational gap by analyzing patient information leaflets (PILs) generated by AI chatbots on EOLC.
UNASSIGNED: Using a comparative research design, PILs generated by ChatGPT and Google Gemini were evaluated for readability, sentiment, accuracy, completeness, and suitability. Readability was assessed using established metrics, sentiment analysis determined emotional tone, accuracy, and completeness were rated by subject experts, and suitability was evaluated using the Patient Education Materials Assessment Tool (PEMAT).
UNASSIGNED: Google Gemini PILs exhibited superior readability and actionability compared to ChatGPT PILs. Both conveyed positive sentiments and high levels of accuracy and completeness, with Google Gemini PILs showing slightly lower accuracy scores.
UNASSIGNED: The findings highlight the promising role of AI in enhancing patient education in EOLC, with implications for improving care outcomes and promoting informed decision-making in diverse cultural settings. Ongoing refinement and innovation in AI-driven patient education strategies are needed to ensure compassionate and culturally sensitive EOLC.
UNASSIGNED: Gondode PG, Khanna P, Sharma P, Duggal S, Garg N. End-of-life Care Patient Information Leaflets-A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini. Indian J Crit Care Med 2024;28(6):561-568.

UNASSIGNED: Smartphone applications aimed at patients with gastroesophageal reflux disease (GERD) have been downloaded more than 100,000 times, yet no systematic assessment of their quality has been completed. This study aimed to objectively assess the quality of GERD smartphone applications for patient education and disease management.
UNASSIGNED: The Apple App Store and Google Play Store were systematically searched for relevant applications. Two independent reviewers performed the application screening and eligibility assessment. Included applications were graded using the validated Mobile Application Rating Scale, which encompasses 4 domains (engagement, functionality, aesthetics, and information) as well as an overall application quality score. The associations between overall application quality, user ratings and download numbers were evaluated.
UNASSIGNED: Of the 4816 unique applications identified, 46 met inclusion criteria (patient education = 37, disease management = 9). Mean overall application quality score was 3.02 ± 0.40 out of 5 (\"acceptable\"), with 61% (28/46) rated as \"poor\" (score 2.0-2.9). Applications scored highest for aesthetics (3.24 ± 0.48) and functionality (3.88 ± 0.37) and lowest for information (2.58 ± 0.64) and engagement (2.39 ± 0.65). Disease management applications were of significantly higher quality than education-focused applications (3.59 ± 0.38 vs 2.88 ± 0.26, P < .001). There was no correlation between graded quality and either user ratings or the number of downloads.
UNASSIGNED: While numerous smartphone applications exist to support patients with GERD, their quality is variable. Patient education applications are of particularly low quality. Our findings can help to inform the selection of applications by patients and guide clinicians\' recommendations. This study also highlights the need for higher-quality, evidence-informed applications aimed at GERD patient education.

Artificial intelligence (AI) and large language models (LLMs) transform how patients inform themselves. LLMs offer potential as educational tools, but their quality depends upon the information generated. Current literature examining AI as an informational tool in dermatology has been limited in evaluating AI\'s multifaceted roles and diversity of opinions. Here, we evaluate LLMs as a patient-educational tool for Mohs micrographic surgery (MMS) in and out of the clinic utilizing an international expert panel.
The most common patient MMS questions were extracted from Google and transposed into two LLMs and Google\'s search engine. 15 MMS surgeons evaluated the generated responses, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response.
The majority of reviewers deemed all LLM responses appropriate. 75% of responses were rated as mostly accurate or higher. ChatGPT had the highest mean accuracy. The majority of the panel deemed 33% of responses sufficient for clinical practice. The mean comprehensibility scores for all platforms indicated a required 10th-grade reading level.
LLM-generated responses were rated as appropriate patient informational sources and mostly accurate in their content. However, these platforms may not provide sufficient information to function in a clinical environment, and complex comprehensibility may represent a barrier to utilization. As the popularity of these platforms increases, it is important for dermatologists to be aware of these limitations.