Flesch Reading Ease Score Research Articles

Evaluating ChatGPT Responses to Frequently Asked Questions Regarding Rhytidectomy

Importance: Patient education is vital during the assessment process. By informing patients, they are better equipped to make well-informed decisions, potentially leading to greater satisfaction after surgery. Objective: The study aimed to assess accuracy and readability, providing a robust evaluation of ChatGPT 3.5’s ability to address medical inquiries and improve the understanding of the facelift for interested patients. Method: On February 2, 2024, an investigation evaluated ChatGPT 3.5’s performance in answering facelift surgery queries. Four commonly asked questions were posed to the model. Responses were collected in an unguided manner and evaluated for accuracy via a literature review. The responses were evaluated for the accuracy with the senior author. Readability was assessed using the Flesch Reading Ease Score, Automated Readability Index, and Flesch-Kincaid Grade Level, calculated through an open-source platform. Result: The AI chatbot demonstrated its ability to generate comprehensive and accurate information, though some responses occasionally required clarification. The readability score and corresponding levels were consistently assessed at the college level. Conclusion: The study’s findings suggest that AI bots can serve as valuable tools for educating and preparing patients interested in getting a facelift.

Improving health literacy and stakeholder-directed knowledge of One Health through analysis of readability: A cross sectional infodemiology study

BackgroundThe One Health approach involves collaboration across several sectors, including public health, veterinary and environmental sectors in an integrated manner. These sectors may be disparate and unrelated, however to succeed, all stakeholders need to understand what the other stakeholders are communicating. Likewise, it is important that there is public acceptance and support of One Health approaches, which requires effective communication between professional and institutional organisations and the public. To help aid and facilitate such communication, written materials need to be readable by all stakeholders, in order to communicate effectively. There has been an exponential increase in the publication of papers involving One Health, with <5 per year, in the 2000s, to nearly 500 published in 2023. To date, readability of One Health information has not been scrutinised, nor has it been considered as an integral intervention of One Health policy communication. The aim of this study was therefore to examine readability of public-facing One Health information prepared by 24 global organisations. MethodsReadability was calculated using Readable software, to obtain four readability scores [(ⅰ) Flesch Reading Ease (FRE), (ⅱ) Flesch-Kinkaid Grade Level (FKGL), (ⅲ) Gunning Fog Index and (ⅳ) SMOG Index] and two text metrics [words/sentence, syllables/word] for 100 sources of One Health information, from four categories [One Health public information, PubMed abstracts; Science in One Health (SOH) abstracts (articles); SOH abstracts (reviews)]. ResultsReadability of One Health information for the public is poor, not reaching readability reference standards. No information was found that had a readability of less than 9th grade (around 14 years old). Mean values for the Flesch Reading Ease and Flesch Kincaid Grade Level were (19.4 ​± ​1.4) (target >60) and (15.6 ​± ​0.3) (target <8), respectively, with mean words per sentence and syllables per word of 20.5 and 2.0, respectively. Abstracts with “One Health” in the title were more difficult to read than those without “One Health” in the title (FRE: P ​= ​0.0337; FKGL: P ​= ​0.0087). Comparison of FRE and FKGL readability scores for the four categories of One Health information [One Health public information, PubMed abstracts; SOH abstracts (articles); SOH abstracts (reviews)] showed that SOH abstracts from articles were easier to read than those from SOH reviews. No One Health public-facing information from the 100 sources examined met the Flesch Kincaid Grade Level target of ≤8. The most easily read One Health information required a Grade Level of 9th grade (14–15 year old), with a mean Grade Level of 15.5 (university/college level). ConclusionConsiderable work is required in making One Health written materials more readable, particularly for children and adolescents (<14 years of age). It is important that any interventions or mitigations taken to support better public understanding of the One Health approach are not ephemeral, but have longer lasting and legacy value. Authors of One Health information should consider using readability calculators when preparing One Health information for their stakeholders, to check the readability of their work, so that the final material is within recommended readability reference parameters, to support the health literacy and stakeholder-directed knowledge of their readers.

Performance of Chatgpt in ophthalmology exam; human versus AI.

This cross-sectional study focuses on evaluating the success rate of ChatGPT in answering questions from the 'Resident Training Development Exam' and comparing these results with the performance of the ophthalmology residents. The 75 exam questions, across nine sections and three difficulty levels, were presented to ChatGPT. The responses and explanations were recorded. The readability and complexity of the explanations were analyzed and The Flesch Reading Ease (FRE) score (0-100) was recorded using the program named Readable. Residents were categorized into four groups based on their seniority. The overall and seniority-specific success rates of the residents were compared separately with ChatGPT. Out of 69 questions, ChatGPT answered 37 correctly (53.62%). The highest success was in Lens and Cataract (77.77%), and the lowest in Pediatric Ophthalmology and Strabismus (0.00%). Of 789 residents, overall accuracy was 50.37%. Seniority-specific accuracy rates were 43.49%, 51.30%, 54.91%, and 60.05% for 1st to 4th-year residents. ChatGPT ranked 292nd among residents. Difficulty-wise, 11 questions were easy, 44 moderate, and 14 difficult. ChatGPT's accuracy for each level was 63.63%, 54.54%, and 42.85%, respectively. The average FRE score of responses generated by ChatGPT was found to be 27.56 ± 12.40. ChatGPT correctly answered 53.6% of questions in an exam for residents. ChatGPT has a lower success rate on average than a 3rd year resident. The readability of responses provided by ChatGPT is low, and they are difficult to understand. As difficulty increases, ChatGPT's success decreases. Predictably, these results will change with more information loaded into ChatGPT.

Comparing Provider and ChatGPT Responses to Breast Reconstruction Patient Questions in the Electronic Health Record.

Patient-directed Electronic Health Record (EHR) messaging is used as an adjunct to enhance patient-physician interactions but further burdens the physician. There is a need for clear electronic patient communication in all aspects of medicine, including plastic surgery. We can potentially utilize innovative communication tools like ChatGPT. This study assesses ChatGPT's effectiveness in answering breast reconstruction queries, comparing its accuracy, empathy, and readability with healthcare providers' responses. Ten deidentified questions regarding breast reconstruction were extracted from electronic messages. They were presented to ChatGPT3, ChatGPT4, plastic surgeons, and advanced practice providers for response. ChatGPT3 and ChatGPT4 were also prompted to give brief responses. Using 1-5 Likert scoring, accuracy and empathy were graded by 2 plastic surgeons and medical students, respectively. Readability was measured using Flesch Reading Ease. Grades were compared using 2-tailed t tests. Combined provider responses had better Flesch Reading Ease scores compared to all combined chatbot responses (53.3 ± 13.3 vs 36.0 ± 11.6, P < 0.001) and combined brief chatbot responses (53.3 ± 13.3 vs 34.7 ± 12.8, P < 0.001). Empathy scores were higher in all combined chatbot than in those from combined providers (2.9 ± 0.8 vs 2.0 ± 0.9, P < 0.001). There were no statistically significant differences in accuracy between combined providers and all combined chatbot responses (4.3 ± 0.9 vs 4.5 ± 0.6, P = 0.170) or combined brief chatbot responses (4.3 ± 0.9 vs 4.6 ± 0.6, P = 0.128). Amid the time constraints and complexities of plastic surgery decision making, our study underscores ChatGPT's potential to enhance patient communication. ChatGPT excels in empathy and accuracy, yet its readability presents limitations that should be addressed.

Enhancing patient understanding in obstetrics: The role of generative AI in simplifying informed consent for labor induction with oxytocin.

Informed consent is a cornerstone of ethical medical practice, particularly in obstetrics where procedures like labor induction carry significant risks and require clear patient understanding. Despite legal mandates for patient materials to be accessible, many consent forms remain too complex, resulting in patient confusion and dissatisfaction. This study explores the use of Generative Artificial Intelligence (GAI) to simplify informed consent for labor induction with oxytocin, ensuring content is both medically accurate and comprehensible at an 8th-grade readability level. GAI-generated consent forms streamline the process, automatically tailoring content to meet readability standards while retaining essential details such as the procedure's nature, risks, benefits, and alternatives. Through iterative prompts and expert refinement, the AI produces clear, patient-friendly language that bridges the gap between medical jargon and patient comprehension. Flesch Reading Ease scores show improved readability, meeting recommended levels for health literacy. GAI has the potential to revolutionize healthcare communication by enhancing patient understanding, promoting shared decision-making, and improving satisfaction with the consent process. However, human oversight remains critical to ensure that AI-generated content adheres to legal and ethical standards. This case study demonstrates that GAI can be an effective tool in creating accessible, standardized, yet personalized consent documents, contributing to better-informed patients and potentially reducing malpractice claims.

Assessing the Responses of Large Language Models (ChatGPT-4, Claude 3, Gemini, and Microsoft Copilot) to Frequently Asked Questions in Retinopathy of Prematurity: A Study on Readability and Appropriateness.

To assess the appropriateness and readability of responses provided by four large language models (LLMs) (ChatGPT-4, Claude 3, Gemini, and Microsoft Co-pilot) to parents' queries pertaining to retinopathy of prematurity (ROP). A total of 60 frequently asked questions were collated and categorized into six distinct sections. The responses generated by the LLMs were evaluated by three experienced ROP specialists to determine their appropriateness and comprehensiveness. Additionally, the readability of the responses was assessed using a range of metrics, including the Flesch-Kincaid Grade Level (FKGL), Gunning Fog (GF) Index, Coleman-Liau (CL) Index, Simple Measure of Gobbledygook (SMOG) Index, and Flesch Reading Ease (FRE) score. ChatGPT-4 demonstrated the highest level of appropriateness (100%) and performed exceptionally well in the Likert analysis, scoring 5 points on 96% of questions. The CL Index and FRE scores identified Gemini as the most readable LLM, whereas the GF Index and SMOG Index rated Microsoft Copilot as the most readable. Nevertheless, ChatGPT-4 exhibited the most intricate text structure, with scores of 18.56 on the GF Index, 18.56 on the CL Index, 17.2 on the SMOG Index, and 9.45 on the FRE score. This suggests that the responses demand a college-level comprehension. ChatGPT-4 demonstrated higher performance than other LLMs in responding to questions related to ROP; however, its texts were more complex. In terms of readability, Gemini and Microsoft Copilot were found to be more successful. [J Pediatr Ophthalmol Strabismus. 20XX;XX(X):XXX-XXX.].

Evaluating the reliability of the responses of large language models to keratoconus-related questions

ABSTRACT Clinical relevance Artificial intelligence has undergone a rapid evolution and large language models (LLMs) have become promising tools for healthcare, with the ability of providing human-like responses to questions. The capabilities of these tools in addressing questions related to keratoconus (KCN) have not been previously explored. Background In this study, the responses were evaluated from three LLMs – ChatGPT-4, Copilot, and Gemini – to common patient questions regarding KCN. Methods Fifty real-life patient inquiries regarding general information, aetiology, symptoms and diagnosis, progression, and treatment of KCN were presented to the LLMs. Evaluations of the answers were conducted by three ophthalmologists with a 5-point Likert scale ranging from ‘strongly disagreed’ to ‘strongly agreed’. The reliability of the responses provided by LLMs was evaluated using the DISCERN and the Ensuring Quality Information for Patients (EQIP) scales. Readability metrics (Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Coleman-Liau Index) were calculated to evaluate the complexity of responses. Results ChatGPT-4 consistently scored 3 points or higher for all (100%) its responses, while Copilot had five (10%) and Gemini had two (4%) responses scoring 2 points or below. ChatGPT-4 achieved a ‘strongly agree’ rate of 74% across all questions, markedly superior to Copilot at 34% and Gemini at 42% (p < 0.001); and recorded the highest ‘strongly agree’ rates in general information and symptoms & diagnosis categories (90% for both). The median Likert scores differed among LLMs (p < 0.001), with ChatGPT-4 scoring highest and Copilot scoring lowest. Although ChatGPT-4 exhibited more reliability based on the DISCERN scale, it was characterised by lower readability and higher complexity. While all LLMs provided responses categorised as ‘extremely difficult to read’, the responses provided by Copilot showed higher readability. Conclusions Despite the responses provided by ChatGPT-4 exhibiting lower readability and greater complexity, it emerged as the most proficient in answering KCN-related questions.

Utilization of Artificial Intelligence to Improve Equitable Healthcare Access for Breast Implant Patients

Abstract The recently mandated US Food and Drug Administration (FDA) patient decision checklists were developed with the goal of improving the informed decision-making process for patients considering breast implants. Yet, prior research has demonstrated that these checklists are written at reading levels far in excess of that recommended by the National Institutes of Health and the American Medical Association. Patients considering breast implants are required to sign these forms prior to surgery, even if they have little to no comprehension of their contents. This study aims to improve the accessibility, and therefore the utility, of the mandated FDA patient literature using the assistance of artificial intelligence (AI). Patient decision checklists were obtained from the three most utilized breast implant manufacturers in the United States– Allergan, Mentor, and Sientra. AI was then asked to synthesize a novel patient decision checklist, written at the 6th grade reading level, using these checklists as source material. The AI-assisted checklist was edited by plastic surgeons for both formatting and content. Readability analysis of all documents was performed using the Flesch Reading-Ease Score, Flesch-Kincaid Grade Level, Gunning-Fog Index, Coleman-Liau Index, Simplified Measure of Gobbledygook, and Automated Readability Index. Textual analysis included sentence count, word count, number and percentage of complex words, average number of words per sentence, and average number of syllables per word. The overall readability of Allergan, Mentor, and Sientra patient checklists correlated with the college reading level. These documents were of a statistically significantly higher reading level the AI-assisted checklist, which was written at the recommended 6th grade level. Text composition analysis similarly demonstrated substantial differences between the AI-assisted and FDA mandated literature. The currently mandated breast implant patient checklists are written at a college reading level and are inaccessible to the average patient. We propose a new patient decision checklist, generated with the assistance of artificial intelligence, in order to improve healthcare access within plastic surgery. This simplified material can be used as an adjunct to the current checklists in order to improve shared decision making.

Improving Biomedical Science Literacy and Patient-Directed Knowledge of Tuberculosis (TB): A Cross-Sectional Infodemiology Study Examining Readability of Patient-Facing TB Information.

Tuberculosis (TB) continues be the leading cause of death globally due to an infectious agent. There is a paucity of data describing the readability of patient-facing TB information for service users. The aim of this study was to calculate the readability of multiple global TB information sources. Information on tuberculosis (n = 150 sources) included nine categories, Patient-facing information: WHO publications (n = 17), International governments (n = 19), Hospitals (n = 10), Non-government organisations (NGOs)/charities (n = 20), Cochrane Plain Language Summaries (n = 20); LabTestsOnlineUK (n = 4) and Scientific-facing information: Clinical trials (n = 20), Cochrane abstracts (n = 20), Scientific abstracts (n = 20). Readability was calculated using Readable software, defined by (i) Flesch Reading Ease (FRE), (ii) Flesch-Kincaid Grade Level (FKGL), (iii) Gunning Fog Index and (iv) SMOG Index and two text metrics [words/sentence, syllables/word]. Mean readability values for TB information for the FRE and FKGL were 35.6 ± 1.6 (standard error of mean (SEM)) (US Target ≥60; UK Target ≥90) and 12.3 ± 0.3 (US Target ≤8; UK Target ≤6), respectively, with mean words per sentence and syllables per word of 17.2 and 1.8, respectively. Cochrane Plain Language Summaries had similar readability scores to their matching scientific abstract (p = 0.15). LabTestsOnlineUK yielded a mean FRE score of 51.5 ± 1.2, a mean FKGL score of 10.2 ± 0.5 and text metric scores of 16.7 ± 2.3 and 1.6, for words per sentence and syllables per word, respectively. In descending order, TB information from international governments, hospitals and LabTestsOnlineUK were the most readable (FRE = 57.9, 54.1 and 51.5, respectively), whereas scientific abstracts and Cochrane abstracts were the most difficult to read (13.0 and 30.2, respectively). Patient-facing TB information analysed had poor readability. Effective communication of biomedical science concepts and information relating to TB is vital for service users to enhance their health literacy of tuberculosis, thereby promoting better clinical outcomes. Biomedical scientists are important custodians of scientific information for their service user populations, including other healthcare professionals within the TB multidisciplinary (MDT) team and patient service users. When preparing TB information, this should be checked and modified in real time employing readability calculators, to align with health readability targets.

Retrograde Cricopharyngeal Dysfunction: A Google Search Analysis.

No studies describe what patients search for online in relation to retrograde cricopharyngeal dysfunction (RCPD). Our objectives were to describe the Google search volume for RCPD, identify the most common queries related to RCPD, and evaluate the available online resources. Observational. Google Database. Using Ahrefs and Search Response, Google search volume for RCPD and "People Also Ask" (PAA) questions were documented. PAA questions were categorized based on intent, and the websites were categorized on source. The quality and readability of the sources were determined using the Journal of the American Medical Association (JAMA) benchmark criteria, Flesch Reading Ease score, and Flesch-Kincaid Grade Level. Search volume for RCPD-related content has continually increased since 2021, with a combined average volume of 6287 searches per month. Most PAA questions were related to technical details (61.07%) and treatments (32.06%) for RCPD. Websites provided to answer these questions were most often from academic (25.95%) and commercial (22.14%) sources. None of the sources met the criteria for universal readability, and only 15% met all quality metrics set forth by JAMA. Interest in RCPD is at an all-time high, with information related to its diagnosis and treatment most popular among Google users. Significantly, none of the resources provided by Google met the criteria for universal readability, preventing many patients from fully comprehending the information presented. Future work should aim to address questions related to RCPD in a suitable way for all patient demographics.

Assessing the Clinical Appropriateness and Practical Utility of ChatGPT as an Educational Resource for Patients Considering Minimally Invasive Spine Surgery.

Introduction Minimally invasive spine surgery (MISS) has evolved over the last three decades as a less invasive alternative to traditional spine surgery, offering benefits such as smaller incisions, faster recovery, and lower complication rates. With patients frequently seeking information about MISS online, the comprehensibility and accuracy of this information are crucial. Recent studies have shown that much of the online material regarding spine surgery exceeds the recommended readability levels, making it difficult for patients to understand. This study explores the clinical appropriateness and readability of responses generated by Chat Generative Pre-Trained Transformer (ChatGPT) to frequently asked questions (FAQs) about MISS. Methods A set of 15 FAQs was formulated based on clinical expertise and existing literature on MISS. Each question was independently inputted into ChatGPT five times, and the generated responses were evaluated by three neurosurgery attendings for clinical appropriateness. Appropriateness was judged based on accuracy, readability, and patient accessibility. Readability was assessed using seven standardized readability tests, including the Flesch-Kincaid Grade Level and Flesch Reading Ease (FRE) scores. Statistical analysis was performed to compare readability scores across preoperative, postoperative, and intraoperative/technical question categories. Results The mean readability scores for preoperative, postoperative, and intraoperative/technical questions were 15±2.8, 16±3, and 15.7±3.2, respectively, significantly exceeding the recommended sixth- to eighth-grade reading level for patient education (p=0.017). Differences in readability across individual questions were also statistically significant (p<0.001). All responses required a reading level above 11th grade, with a majority indicating college-level comprehension. Although preoperative and postoperative questions generally elicited clinically appropriate responses, 50% of intraoperative/technical questions yielded either "inappropriate" or "unreliable" responses, particularly for inquiries about radiation exposure and the use of lasers in MISS. Conclusions While ChatGPT is proficient in providing clinically appropriate responses to certain FAQs about MISS, it frequently produces responses that exceed the recommended readability level for patient education. This limitation suggests that its utility may be confined to highly educated patients, potentially exacerbating existing disparities in patient comprehension. Future AI-based patient education tools must prioritize clear and accessible communication, with oversight from medical professionals to ensure accuracy and appropriateness. Further research comparing ChatGPT's performance with other AI models could enhance its application in patient education across medical specialties.

Appropriateness and readability of Google Bard and ChatGPT-3.5 generated responses for surgical treatment of glaucoma.

To evaluate the appropriateness and readability of the medical knowledge provided by ChatGPT-3.5 and Google Bard, artificial-intelligence-powered conversational search engines, regarding surgical treatment for glaucoma. In this retrospective, cross-sectional study, 25 common questions related to the surgical management of glaucoma were asked on ChatGPT-3.5 and Google Bard. Glaucoma specialists graded the responses' appropriateness, and different scores assessed readability. Appropriate answers to the posed questions were obtained in 68% of the responses with Google Bard and 96% with ChatGPT-3.5. On average, the responses generated by Google Bard had a significantly lower proportion of sentences, having more than 30 and 20 syllables (23% and 52% respectively) compared to ChatGPT-3.5 (66% and 82% respectively), as noted by readability. Google Bard had significantly (p<0.0001) lower readability grade scores and significantly higher "Flesch Reading ease score", implying greater ease of readability amongst the answers generated by Google Bard. Many patients and their families turn to LLM chatbots for information, necessitating clear and accurate content. Assessments of online glaucoma information have shown variability in quality and readability, with institutional websites generally performing better than private ones. We found that ChatGPT-3.5, while precise, has lower readability than Google Bard, which is more accessible but less precise. For example, the Flesch Reading Ease Score was 57.6 for Google Bard and 22.6 for ChatGPT, indicating Google Bard's content is easier to read. Moreover, the Gunning Fog Index scores suggested that Google Bard's text is more suitable for a broader audience. ChatGPT's knowledge is limited to data up to 2021, whereas Google Bard, trained with real-time data, offers more current information. Further research is needed to evaluate these tools across various medical topics. The answers generated by ChatGPT-3.5™ AI are more accurate than the ones given by Google Bard. However, comprehension of ChatGPT-3.5™ answers may be difficult for the public with glaucoma. This study emphasized the importance of verifying the accuracy and clarity of online information that glaucoma patients rely on to make informed decisions about their ocular health. This is an exciting new area for patient education and health literacy.

Patient-friendliness of ChatGPT responses: NSCLC targeted therapy.

231 Background: Non-small cell lung cancer (NSCLC) targeted therapies are complex, often requiring extensive patient education beyond what can be conveyed in a single provider visit. Given the average US reading level is at the 8th grade, and many patients seek additional information online, it is vital to evaluate the readability of online resources. This study aims to determine the patient friendliness of information provided by AI chatbots like ChatGPT in explaining NSCLC targeted therapies compared to other commonly accessed sources. Methods: This analysis included targeted therapies approved by the FDA for EGFR-mutated NSCLC: Afatinib, Erlotinib, Osimertinib, Dacomitinib, and Gefitinib. Information sources included ChatGPT 4, ChatGPT 3.5, the Patient Information section of the FDA label, and the Google featured snippet. Each version of ChatGPT was queried 10 times for each individual therapy – “My doctor is suggesting starting therapy with (Drug Name). What can I expect with this medication?”. All responses, labels, and snippets were examined by Microsoft Word for word count (WC) and Flesch Reading Ease Score (FRES), which considers average sentence length and number of syllables per word to assess readability. Data analysis was performed using ANOVA in SAS. Results: Across all therapies, Mean FRES for ChatGPT 4 was 31.21, ChatGPT 3.5 26.69, FDA labels 50.78, and Google snippets 33.1. Mean WC for ChatGPT 4 was 312.54, ChatGPT 3.5 355.78, FDA labels 1220, and Google snippets 42. Significant differences in FRES were found among sources for Afatinib (p-value &lt;0.0001), Erlotinib (p-value 0.0206), Osimertinib (p-value &lt;0.0001), Dacomitinib (p-value 0.0125), and Gefitinib (p-value &lt;0.0001). Significant differences in WC were also found among sources for Afatinib (p-value &lt;0.0001), Erlotinib (p-value &lt;0.0001), Osimertinib (p-value &lt;0.0001), and Gefitinib (p-value 0.0045). No significant difference in WC was found for Dacomitinib (p-value 0.2807). Conclusions: While the FDA label provided the most readable information, none of the sources aligned with the average 8th-grade reading level in the US, indicating a gap in accessible patient education for NSCLC targeted therapies. Notably, readability varies significantly between ChatGPT versions, affecting accessibility for users of the free version, ChatGPT 3.5. Although ChatGPT responses are more succinct compared to the verbose FDA labels, their higher education-level readability presents a barrier to comprehension. This study highlights the need for further research into optimizing AI-generated patient education materials to be both accurate and comprehensible at appropriate reading levels, thereby improving health communication strategies.

A Critical Assessment of the Quality and Readability of Information on Gastric Cancer Available on the Internet for Patients.

Introduction It is now commonplace for patients to consult the internet with health-related questions. Unfortunately, the quality of information provided to them online is highly variable. Ensuring that patients get high-quality, reliable information is essential for all pathologies. Gastric cancer (GC), with its often subtle early symptoms and signs, is one such pathology where early identification is crucial. Ensuring high-quality information availability online for GC is thus essential to increasing rates of early detection. Aims This study aimed to assess the quality and readability of information posted on websites related to GC. Materials and methods We applied the search term "gastric cancer" or "stomach cancer" to the top three search engines, namely Google, Yahoo, and Bing. Using predefined inclusion and exclusion criteria, we identified 20 unique websites posting information related to gastric cancer (GC). We then assessed the quality and readability of the information posted on these websites. We used recognized tools to complete these assessments, including the JAMA benchmark criteria, the DISCERN tool, the Flesch Reading Ease score (FRES), and the Flesch-Kincaid Grade Level (FKGL). We also developed and used a novel GC-specific content assessment tool. Furthermore, we assessed whether or not each website was awarded the Health on the Internet Seal of Approval. Results The average JAMA score was 1.55, with none of the twenty unique websites scoring the maximum 4 points. The average DISCERN score was 54.8 (68.5%), with no website achieving the maximum of 80. The HON seal was present in only six websites (30%). The average GCSCS score was 11, with only five websites achieving a maximum score of 13 (25%). The average FRES and FKGL were 52.7 and 9.7, respectively. Conclusion Our study underscores the critical need for more high-quality, reliable information about GC online. We also emphasize the importance of ensuring this information is comprehensible to most patients, as it directly impacts their health outcomes.

Cultural adaptation and validation of the French version of the Antwerp pelvic floor knowledge questionnaire

AimsThe Antwerp Pelvic Floor Knowledge Questionnaire (APFK-Q) is a self-administered tool used for assessing women’s knowledge of the pelvic floor, an essential element of pelvic health education initiatives. The aim of this study was to validate the French version of the APFK-Q and to assess its psychometric properties. MethodsAcceptability of the cultural adaptation of APFK-Q in French (translation/back-translation) was assessed in two groups of women: one group not working in the healthcare sector (non-healthworkers, n = 112), and another of student midwives (n = 29). Twenty-three of the non-healthworkers agreed to take part in a test–retest step to assess reproducibility. Agreement of responses was measured for each question using Cohen’s kappa coefficient. Sensitivity to change was assessed before and after a pelvic-floor health education workshop. The proportion of correct answers was compared before and after the workshop using McNemar’s Chi2 test. Readability and the reading level required for optimal comprehension were assessed using the Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level test, respectively. ResultsSeventy percent (n = 72) of the non-healthworkers and 72 % (n = 21) of the students reported no comprehension difficulties. Agreement was very good (k > 0.8) for 46 % of the items and good (0.6–0.8) for 25 %. The proportion of correct answers was significantly higher for 72 % of the items after the pelvic-floor health education workshop. The French version of the APFK-Q was judged to be easy to understand according to the FRES test, with a score of 60.6. The school level required for optimal comprehension was estimated at 8th grade. ConclusionThe French version of the APFK-Q is an acceptable, reproducible, and easily understandable questionnaire that provides a broad description of women’s knowledge of the pelvic-floor sphere. Sensitivity to change was confirmed for most items, making it a useful tool for assessing the impact of pelvic-floor health education initiatives.

Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients

BackgroundSearch engines often serve as a primary resource for patients to obtain drug information. However, the search engine market is rapidly changing due to the introduction of artificial intelligence (AI)-powered...

Assessing the performance of AI chatbots in answering patients’ common questions about low back pain

ObjectivesThe aim of this study was to assess the accuracy and readability of the answers generated by large language model (LLM)-chatbots to common patient questions about low back pain (LBP).MethodsThis...

Expanding Accessibility in Cleft Care: The Role of Artificial Intelligence in Improving Literacy of Alveolar Bone Grafting Information.

The American Medical Association (AMA) recommends patient education materials (PEMs) be written at or below a sixth grade reading level. This study seeks to determine the quality, readability, and content of available alveolar bone grafting (ABG) PEMs and determine if artificial intelligence can improve PEM readability. Review of free online PEMs. Online ABG PEMs were retrieved from different authoring body types (hospital/academic center, medical society, or private practice). None. Content was assessed by screening PEMs for specific ABG-related topics. Quality was evaluated with the Patient Education Material Assessment Tool (PEMAT), which has measures of understandability and actionability. Open-access readability software (WebFX) determined readability with Flesch Reading Ease, Flesch-Kincaid Grade Level, and Gunning-Fog Index. PEMs were rewritten with ChatGPT, and readability metrics were reassessed. Quality, readability, and content of ABG PEMs. 34 PEMs were analyzed. Regarding quality, the average PEMAT-understandability score was 67.0 ± 16.2%, almost at the minimum acceptable score of 70.0% (p = 0.281). The average PEMAT-actionability score was low at 33.0 ± 24.1%. Regarding readability, the average Flesch Reading Ease score was 64.6 ± 12.8, categorized as "standard/plain English." The average Flesch-Kincaid Grade Level was 8.0 ± 2.3, significantly higher than AMA recommendations (p < 0.0001). PEM rewriting with ChatGPT improved Flesch-Kincaid Grade Level to 6.1 ± 1.3 (p < 0.0001). Available ABG PEMs are above the recommended reading level, yet ChatGPT can improve PEM readability. Future studies should improve areas of ABG PEMs that are most lacking, such as actionability.

Evaluation of the quality of Internet information on masseter botox injection for bruxism.

Botulinum toxin, commonly referred to as botox, is frequently used in facial aesthetics, but also has therapeutic applications in various medical and surgical conditions such as bruxism treatment. The aim of this research is to evaluate the information quality of only English resources available on the Internet regarding the use of botulinum toxin in the treatment of bruxism. The terms "Botox + Tooth Grinding" selected for this study were searched on the Internet using the Google Search Engine, and the URLs of the top 150 websites containing only English pages were recorded and examined. The websites were assessed using DISCERN tools, the Journal of the American Medical Association (JAMA) benchmark, and HONcode tools. The website readability evaluation involved utilizing the Flesch Reading Ease Score (FRES) and the Flesch-Kincaid Grade Level (FKGL). A total of 130 websites met the inclusion criteria. The total mean DISCERN score (section 1 + section 2) for all websites was 39.75/75 (range: 15-67). The average FRES of all websites was 52.79, and the average FKGL score of all websites was 10.72. Internet blog websites had significantly higher DISCERN (Section1) scores than other websites (p= 0.007). Only one website had the HON code. Internet blog websites showed better compliance with JAMA (authorship and currency) benchmarks. No statistically significant difference was determined related to FRES and FKGL scores between groups (p> 0.05). There is moderate and difficult-to-read information on the Internet about masseter botox injection for bruxism. Relevant guidelines are needed to improve the quality of websites about botulinum toxin for the treatment of bruxism.

Digesting Digital Health: A Study of Appropriateness and Readability of ChatGPT-Generated Gastroenterological Information.

The advent of artificial intelligence-powered large language models capable of generating interactive responses to intricate queries marks a groundbreaking development in how patients access medical information. Our aim was to evaluate the appropriateness and readability of gastroenterological information generated by ChatGPT. We analyzed responses generated by ChatGPT to 16 dialogue-based queries assessing symptoms and treatments for gastrointestinal conditions and 13 definition-based queries on prevalent topics in gastroenterology. Three board-certified gastroenterologists evaluated output appropriateness with a 5-point Likert-scale proxy measurement of currency, relevance, accuracy, comprehensiveness, clarity, and urgency/next steps. Outputs with a score of 4 or 5 in all 6 categories were designated as "appropriate." Output readability was assessed with Flesch Reading Ease score, Flesch-Kinkaid Reading Level, and Simple Measure of Gobbledygook scores. ChatGTP responses to 44% of the 16 dialogue-based and 69% of the 13 definition-based questions were deemed appropriate, and the proportion of appropriate responses within the 2 groups of questions was not significantly different (P = .17). Notably, none of ChatGTP's responses to questions related to gastrointestinal emergencies were designated appropriate. The mean readability scores showed that outputs were written at a college-level reading proficiency. ChatGPT can produce generally fitting responses to gastroenterological medical queries, but responses were constrained in appropriateness and readability, which limits the current utility of this large language model. Substantial development is essential before these models can be unequivocally endorsed as reliable sources of medical information.