Common Questions Research Articles

Is ChatGPT an Accurate and Readable Patient Aid for Third Molar Extractions?

Artificial intelligence (AI) platforms such as Chat Generative Pre-Trained Transformer (ChatGPT) (Open AI, San Francisco, California, USA) have the capacity to answer health-related questions. It remains unknown whether AI can be a patient-friendly and accurate resource regarding third molar extraction. The purpose was to determine the accuracy and readability of AI responses to common patient questions regarding third molar extraction. This is a cross sectional in-silico assessment of readability and soundness of a computer-generated report. Not applicable. Accuracy, or the ability to provide clinically correct and relevant information, was determined subjectively by 2 reviewers using a 5-point Likert scale, and objectively by comparing responses to American Association of Oral and Maxillofacial Surgeons (AAOMS) clinical consensus papers. Readability, or how easy a piece of text is to read, was assessed using the Flesch Kincaid Reading Ease (FKRE) and Flesch Kincaid Grade Level (FKGL). Both assess readability based on mean number of syllables per word, and words per sentence. To be deemed "readable", FKRE should be >60 and FKGL should be <8. Not applicable. Descriptive statistics were used to analyze the findings of this study. AI-generated responses above the recommended level for the average patient (FKRE: 52; FKGL: 10). The average Likert score was 4.36, suggesting that most responses were accurate with minor inaccuracies or missing information. AI correctly deferred to the provider in instances where no definitive answer exists. Of the responses that addressed content in AAOMS consensus papers, 18/19 responses closely aligned with them. All prompts did not provide citations or references. AI was able to provide mostly accurate responses, and content was closely aligned with AAOMS guidelines. However, responses were too complex for the average third molar extraction patient, and were deficient in citations and references. It is important for providers to educate patients on the utility of AI, and to decide whether to recommend using it for information. Ultimately, the best resource for answers is from the practitioners themselves because the AI platform lacks clinical experience.

Understanding How ChatGPT May Become a Clinical Administrative Tool Through an Investigation on the Ability to Answer Common Patient Questions Concerning Ulnar Collateral Ligament Injuries.

The consumer availability and automated response functions of chat generator pretrained transformer (ChatGPT-4), a large language model, poise this application to be utilized for patient health queries and may have a role in serving as an adjunct to minimize administrative and clinical burden. To evaluate the ability of ChatGPT-4 to respond to patient inquiries concerning ulnar collateral ligament (UCL) injuries and compare these results with the performance of Google. Cross-sectional study. Google Web Search was used as a benchmark, as it is the most widely used search engine worldwide and the only search engine that generates frequently asked questions (FAQs) when prompted with a query, allowing comparisons through a systematic approach. The query "ulnar collateral ligament reconstruction" was entered into Google, and the top 10 FAQs, answers, and their sources were recorded. ChatGPT-4 was prompted to perform a Google search of FAQs with the same query and to record the sources of answers for comparison. This process was again replicated to obtain 10 new questions requiring numeric instead of open-ended responses. Finally, responses were graded independently for clinical accuracy (grade 0 = inaccurate, grade 1 = somewhat accurate, grade 2 = accurate) by 2 fellowship-trained sports medicine surgeons (D.W.A, J.S.D.) blinded to the search engine and answer source. ChatGPT-4 used a greater proportion of academic sources than Google to provide answers to the top 10 FAQs, although this was not statistically significant (90% vs 50%; P = .14). In terms of question overlap, 40% of the most common questions on Google and ChatGPT-4 were the same. When comparing FAQs with numeric responses, 20% of answers were completely overlapping, 30% demonstrated partial overlap, and the remaining 50% did not demonstrate any overlap. All sources used by ChatGPT-4 to answer these FAQs were academic, while only 20% of sources used by Google were academic (P = .0007). The remaining Google sources included social media (40%), medical practices (20%), single-surgeon websites (10%), and commercial websites (10%). The mean (± standard deviation) accuracy for answers given by ChatGPT-4 was significantly greater compared with Google for the top 10 FAQs (1.9 ± 0.2 vs 1.2 ± 0.6; P = .001) and top 10 questions with numeric answers (1.8 ± 0.4 vs 1 ± 0.8; P = .013). ChatGPT-4 is capable of providing responses with clinically relevant content concerning UCL injuries and reconstruction. ChatGPT-4 utilized a greater proportion of academic websites to provide responses to FAQs representative of patient inquiries compared with Google Web Search and provided significantly more accurate answers. Moving forward, ChatGPT has the potential to be used as a clinical adjunct when answering queries about UCL injuries and reconstruction, but further validation is warranted before integrated or autonomous use in clinical settings.

THE SERBIAN LANGUAGE IN THE EYES OF FOREIGNERS: ANALYSIS OF THE MOST COMMON DOUBTS OF LEARNERS ON THE SOCIAL PLATFORM REDDIT

The subject of this paper is the analysis of posts on Reddit about foreigners’ confusions related to learning Serbian as a foreign language. The aim of the research is to clarify the most common questions of foreigners who are interested in learning the Serbian language. A descriptive method and a method of theoretical analysis with the technique of content analysis have been used in the paper. The analysis of research results has shown that there has been a lot of interest among foreigners for learning Serbian as a foreign language. The most common questions related to mastering the Serbian language include: 1. issues of integration into the social community (solving administrative problems, studying and working, finding friends, finding information about the character and mentality of the Serbian people, getting to know the culture and history of the Serbian people, life in Serbia without knowing the Serbian language, etc.), 2. issues of finding language learning modalities (accredited study centers, private schools, online platforms, private lessons), 3. issues of learning Serbian as a foreign language (difficulty in learning, time needed to master the language, differences between the eastern and western variants of the language), 4. issues of finding and using appropriate teaching tools (textbooks, language learning apps, podcasts, adapted folk and art texts, music, films and TV shows with foreign language translation, flash cards, illustrations and tables) and 5. the issue of mastering certain grammatical and lexical contents (Cyrillic, cases, difference between perfective and imperfective verbs, getting to know colloquial expressions).

Appraisal of ChatGPT's responses to common patient questions regarding Tommy John surgery.

Artificial intelligence (AI) has progressed at a fast pace. ChatGPT, a rapidly expanding AI platform, has several growing applications in medicine and patient care. However, its ability to provide high-quality answers to patient questions about orthopedic procedures such as Tommy John surgery is unknown. Our objective is to evaluate the quality of information provided by ChatGPT 3.5 and 4.0 in response to patient questions regarding Tommy John surgery. Twenty-five patient questions regarding Tommy John surgery were posed to ChatGPT 3.5 and 4.0. Readability was assessed via Flesch Kincaid Reading Ease, Flesh Kinkaid Grade Level, Gunning Fog Score, Simple Measure of Gobbledygook, Coleman Liau, and Automated Readability Index. The quality of each response was graded using a 5-point Likert scale. ChatGPT generated information at an educational level that greatly exceeds the recommended level. ChatGPT 4.0 produced slightly better responses to common questions regarding Tommy John surgery with fewer inaccuracies than ChatGPT 3.5. Although ChatGPT can provide accurate information regarding Tommy John surgery, its responses may not be easily comprehended by the average patient. As AI platforms become more accessible to the public, patients must be aware of their limitations.

Knock down to level up: Reframing RNAi for invertebrate ecophysiology

Comparative ecophysiologists strive to understand physiological problems in non-model organisms, but molecular tools such as RNA interference (RNAi) are under-used in our field. Here, we provide a framework for invertebrate ecophysiologists to use RNAi to answer questions focused on physiological processes, rather than as a tool to investigate gene function. We specifically focus on non-model invertebrates, in which the use of other genetic tools (e.g., genetic knockout lines) is less likely. We argue that because RNAi elicits a temporary manipulation of gene expression, and resources to carry out RNAi are technically and financially accessible, it is an effective tool for invertebrate ecophysiologists. We cover the terminology and basic mechanisms of RNA interference as an accessible introduction for “non-molecular” physiologists, include a suggested workflow for identifying RNAi gene targets and validating biologically relevant gene knockdowns, and present a hypothesis-testing framework for using RNAi to answer common questions in the realm of invertebrate ecophysiology. This review encourages invertebrate ecophysiologists to use these tools and workflows to explore physiological processes and bridge genotypes to phenotypes in their animal(s) of interest.

Analyzing Large Language Models' Responses to Common Lumbar Spine Fusion Surgery Questions: A Comparison Between ChatGPT and Bard.

In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Our study aims to assess the response quality of Open AI (artificial intelligence)'s ChatGPT 3.5 and Google's Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from 'unsatisfactory' to 'excellent.' The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard's responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs' role in medical education and healthcare communication.

When IBD Patients Require an Ostomy: Evidence-based Answers to 10 Common Clinical Questions in IBD Surgery

When IBD Patients Require an Ostomy: Evidence-based Answers to 10 Common Clinical Questions in IBD Surgery

Foundations &amp; Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this article is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining three key principles of modality heterogeneity , connections , and interactions that have driven subsequent innovations, and propose a taxonomy of six core technical challenges: representation , alignment , reasoning , generation , transference , and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy.

Prospects using chat bots as a tool for training middle-level specialists

The subject of the research is the use of artificial intelligence in the educational process. The relevance is due to the need to introduce new technologies in order to improve the quality of training of mid-level specialists. Novelty and practical significance: the introduction of artificial intelligence helps to optimize the practical training of students.Purpose of the research — to identify the attitude of teachers and students of the Medical College of Railway Transport to the use of artificial intelligence in the field of education.Modern realities provide for the use of e-learning and distance technologies in education. The need for tools and services to convey knowledge via the Internet has increased with the transition of education to an online format. It is known the students spend a lot of time in messengers. An educational chatbot can become a powerful tool for online learning. This format makes learning more effective. One of the main advantages of introducing and using chatbots in education is the automation of processes in which both students and teachers, administrators, and employers participate.Practical significance — a chat bot was created in the Medical College of Railway Transport in order to test the use of artificial intelligence in organizing practical training on the social network “VKontakte” in the community of MK ZhT, confirmed by “State Services”. A chat bot answers the most common questions of students, providing round-the-clock information support.

ChatGPT can yield valuable responses in the context of orthopaedic trauma surgery.

To assess the possibility of using Generative Pretrained Transformer (ChatGPT) specifically in the context of orthopaedic trauma surgery by questions posed to ChatGPT and to evaluate responses (correctness, completenessand adaptiveness) by orthopaedic trauma surgeons. ChatGPT (GPT-4 of 12 May 2023) was asked to address 34 common orthopaedic trauma surgery-related questions and generate responses suited to three target groups: patient, nonorthopaedic medical doctorand expert orthopaedic surgeon. Three orthopaedic trauma surgeons independently assessed ChatGPT's responses by using a three-point response scale with a response range between 0 and 2, where a higher number indicates better performance (correctness, completenessand adaptiveness). A total of 18 (52.9%) of all responses were assessed to be correct (2.0) for the patient target group, while 22 (64.7%) and 24 (70.5%) of the responses were determined to be correct for nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Moreover, a total of 18 (52.9%), 25 (73.5%)and 28 (82.4%) of the responses were assessed to be complete (2.0) for patients, nonorthopaedic medical doctorsand expert orthopaedic surgeons, respectively. The average adaptiveness was 1.93, 1.95and 1.97 for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. The study results indicate that ChatGPT can yield valuable and overall correct responses in the context of orthopaedic trauma surgery across different target groups, which encompassed patients, nonorthopaedic medical surgeonsand expert orthopaedic surgeons. The average correctness scores, completeness levelsand adaptiveness values indicated the ability of ChatGPT to generate overall correct and complete responses adapted to the target group. Not applicable.

Reconceptualising the EU-member states relationship in the age of permanent emergency

AbstractSince 2008, the European Union (EU) has been engulfed in several crises. While distinct, these crises are feeding into each other and are testing the capacity and resilience of EU and member states, bringing forward common policy questions and new forms of cooperation. The permanent character of this state of crisis has made analysts talk about our times as the age of permacrisis. As a result, a trend towards a new mode of ‘coordinative Europeanization’ in EU decision-making has been observed since the Covid-19 crisis. In this paper we first define the new mode of coordinative Europeanization and outline its key features. We argue that the recent crises have altered the EU member states’ relationship in pursuit of fast policy responses. We analyse the reasons behind this changing relationship as well as the possible avenues it may take. We then discuss the challenges caused by coordinative Europeanization and how it relates to pre-existing de-Europeanization tendencies. We close the paper with an analysis of the significance of our findings and we propose new avenues for research.

Legal Issues 101: Implications of Attendance, Absenteeism, and Truancy in the School Setting

This article is part of Legal Issues 101, a series of topics addressing some common questions and misconceptions about the law and school health. School nurse job responsibilities differ regarding monitoring student attendance and assisting school administrators with required truancy reporting. However, all school nurses support student attendance with a focus on students being safe, healthy, and ready to learn. In this article, the authors address frequently asked questions on topics surrounding student attendance including excused and unexcused absences, truancy, parent/guardian engagement, and related laws.

Perceptions of Dysphagia Evaluation and Treatment Among Individuals with Parkinson's Disease.

Dysphagia is a leading cause of morbidity and mortality among individuals with Parkinson's disease (PD). The primary objectives of the present study were to explore patients' narrative reports focused on what information and evaluation and treatment experiences they identified as they manage dysphagia, and to identify practice patterns relevant to dysphagia management. A secondary objective was to produce an educational resource for this population that addressed their questions about dysphagia. A sample of individuals with oropharyngeal dysphagia secondary to PD (n = 25) across all regions of the United States were interviewed using open- and closed questions and a written questionnaire. Verbatim interview transcripts were interrogated using qualitative content analysis (QCA) with an inductive approach to identify themes from the participants' reported knowledge of dysphagia and experiences with swallowing evaluation and treatment. Authors developed a pamphlet addressing common questions that participants posed in the interviews and conducted a member check to revise it with their feedback. Most participants reported having been asked about their swallowing function by a healthcare professional. 60% of the sample reported having had a swallowing evaluation. Only 20% (5/25) of participants reported having completed swallowing therapy. Some participants did not know that swallowing therapy exists. Nearly all participants reported having a strong desire to know more about dysphagia and preferred a pamphlet as a resource format. Few of the study participants had received swallowing therapy, and nearly all participants were eager to learn about the nature of dysphagia, its progression, and treatment options. Given the physical, emotional, and social ramifications of living with dysphagia, access to swallowing education and treatment needs to be a stronger focus of PD management.

ChatGPT Provides Satisfactory but Occasionally Inaccurate Answers to Common Patient Hip Arthroscopy Questions

PurposeTo assess the ability of ChatGPT to answer common patient questions regarding hip arthroscopy, and to analyze the accuracy and appropriateness of its responses. MethodsTen questions were selected from well-known patient education websites, and ChatGPT (version 3.5) responses to these questions were graded by two fellowship-trained hip preservation surgeons. Responses were analyzed, compared to the current literature, and graded from A to D (A being the highest, and D being the lowest) in a grading scale based on the accuracy and completeness of the response. If the grading differed between the two surgeons, a consensus was reached. Inter-rater agreement was calculated. The readability of responses was also assessed using the Flesch-Kincaid Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL). ResultsResponses received the following consensus grades: A (50%, n=5), B (30%, n=3), C (10%, n=1), D (10%, n=1) (Table 2). Inter-rater agreement based on initial individual grading was 30%. The mean FRES was 28.2 (SD± 9.2), corresponding to a college graduate level, ranging from 11.7 to 42.5. The mean FKGL was 14.4 (SD±1.8), ranging from 12.1 to 18, indicating a college student reading level. ConclusionChatGPT can answer common patient questions regarding hip arthroscopy with satisfactory accuracy graded by two high-volume hip arthroscopists, however, incorrect information was identified in more than one instance. Caution must be observed when using ChatGPT for patient education related to hip arthroscopy. Clinical RelevanceGiven the increasing number of hip arthroscopies being performed annually, ChatGPT has the potential to aid physicians in educating their patients about this procedure and address any questions they may have.

Updates to a pharmacy-technician driven centralized medication refill service in a large community health system

In 2020, we published a description of the newly implemented centralized refill service (CRS) led by pharmacy technicians in our large community health system. Since that time, the CRS has been refined, updated, and expanded. We have also received many inquiries with common questions about the process from those who seek to implement a similar process. The purpose of this commentary is to 1) provide updates to the process in the 5 years since its implementation, and 2) provide additional insights on specific topics from inquiries to the organization.

Assessing the Impact of the 340B Drug Pricing Program: A Scoping Review of the Empirical, Peer-Reviewed Literature.

Despite remarkable growth and relevance of the 340B Drug Pricing Program to current health care practice and policy debate, academic literature examining 340B has lagged. The objectives of this scoping review were to summarize i) common research questions published about 340B, ii) what is empirically known about 340B and its implications, and iii) remaining knowledge gaps, all organized in a way that is informative to practitioners, researchers, and decision makers. We conducted a scoping review of the peer-reviewed, empirical 340B literature (database inception to March 2023). We categorized studies by suitability of their design for internal validity, type of covered entity studied, and motivation-by-scope category. The final yield included 44 peer-reviewed, empirical studies published between 2003 and 2023. We identified 15 frequently asked research questions in the literature, across 6 categories of inquiry-motivation (margin or mission) and scope (external, covered entity, and care delivery interface). Literature with greatest internal validity leaned toward evidence of margin-motivated behavior at the external environment and covered entity levels, with inconsistent findings supporting mission-motivated behavior at these levels; this was particularly the case among participating disproportionate share hospitals (DSHs). However, included case studies were unanimous in demonstrating positive effects of the 340B program for carrying out a provider's safety net mission. In our scoping review of the 340B program, the highest-quality evidence indicates nonprofit, DSHs may be using the 340B program in margin-motivated ways, with inconsistent evidence for increased safety net engagement; however, this finding is not consistent across other hospital types and public health clinics, which face different incentive structures and reporting requirements. Future studies should examine heterogeneity by covered entity types (i.e., hospitals vs. public health clinics), characteristics, and time period of 340B enrollment. Our findings provide additional context to current health policy discussion regarding the 340B program.

ChatGPT-4 Performs Clinical Information Retrieval Tasks Using Consistently More Trustworthy Resources Than Does Google Search for Queries Concerning the Latarjet Procedure

PurposeTo assess the ability for ChatGPT-4, an automated Chatbot powered by artificial intelligence (AI), to answer common patient questions concerning the Latarjet procedure for patients with anterior shoulder instability and compare this performance to Google Search Engine. MethodsUsing previously validated methods, a Google search was first performed using the query “Latarjet.” Subsequently, the top ten frequently asked questions (FAQs) and associated sources were extracted. ChatGPT-4 was then prompted to provide the top ten FAQs and answers concerning the procedure. This process was repeated to identify additional FAQs requiring discrete-numeric answers to allow for a comparison between ChatGPT-4 and Google. Discrete, numeric answers were subsequently assessed for accuracy based on the clinical judgement of two fellowship-trained sports medicine surgeons blinded to search platform. ResultsMean (±standard deviation) accuracy to numeric-based answers were 2.9±0.9 for ChatGPT-4 versus 2.5±1.4 for Google (p=0.65). ChatGPT-4 derived information for answers only from academic sources, which was significantly different from Google Search Engine (p=0.003), which used only 30% academic sources and websites from individual surgeons (50%) and larger medical practices (20%). For general FAQs, 40% of FAQs were found to be identical when comparing ChatGPT-4 and Google Search Engine. In terms of sources used to answer these questions, ChatGPT-4 again used 100% academic resources, while Google Search Engine used 60% academic resources, 20% surgeon personal websites, and 20% medical practices (p=0.087). ConclusionChatGPT-4 demonstrated the ability to provide accurate and reliable information about the Latarjet procedure in response to patient queries, using multiple academic sources in all cases. This was in contrast to Google Search Engine, which more frequently used single surgeon and large medical practice websites. Despite differences in the resources accessed to perform information retrieval tasks, the clinical relevance and accuracy of information provided did not significantly differ between ChatGPT-4 and Google Search Engine.

Practice Pearls for Stimulant Treatment of Attention-Deficit/Hyperactivity Disorder in Youth.

Over half of youth with attention-deficit/hyperactivity disorder (ADHD) have co-occurring psychiatric or medical conditions that present treatment challenges. Stimulants are the most effective pharmacologic treatment of ADHD for preschoolers to adults but questions about safety with co-occurring conditions frequently arise. In addition, stigma surrounding diagnosis and treatment can negatively impact care. This manuscript presents evidence-based practice pearls to guide treatment decisions for youth with ADHD and common coexisting psychiatric and medical conditions. Recommendations address specific stimulant adverse effects (i.e., anxiety, cardiac, growth, mania, psychosis) along with management strategies. Pearls were developed for the most common clinical questions, controversial topics, or therapeutic issues that may not be widely known. The goals of this manuscript are to: 1) provide a detailed resource for interprofessional teams regarding stimulant use in youth with ADHD, 2) improve therapeutic outcomes for youth with ADHD and co-occurring psychiatric and/or medical conditions through evidence-based recommendations, and 3) decrease stigma associated with stimulant use through education.

Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome.

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. To assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches. Cross-sectional study. 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. 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. 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.

Performance of an online chat-based artificial intelligence interface for patient education on atrial fibrillation ablation

Abstract Background Chat-based artificial intelligence (AI) web interfaces that aim to mimic human conversation have increasing utilization in healthcare to help with simple tasks such as scheduling appointments, and even more complex tasks such as providing patient educational responses to COVID-19 questions as done by the World Health Organization.1 Chat-based AI has also been shown to provide accurate responses to cardiovascular disease prevention questions.2 Its ability to provide patient education for more complex treatments like atrial fibrillation (AF) ablation has not been explored. Purpose To evaluate the quality of a popular chat-based AI program’s answers to patient questions about AF ablation. Methods Twenty commonly asked questions ("prompts") regarding AF ablation were entered into ChatGPT (Chat Generative Pre-trained Transformer), a large language model-based AI program (Fig. 1). Prompts were written in plain language; technical terms were avoided except for "radiofrequency", "cryoablation" and "pulsed field ablation" (PFA). SMOG readability calculator was used to assess responses for difficulty and grade-level, as healthcare organizations recommend ≤ 8th-grade level complexity for patient information. Response content was graded by 3 experienced cardiac electrophysiologists as "reasonable", "missing important elements/some inaccuracies" or "misleading/inappropriate". Responses are presented in mean +/- standard deviation and percentages. Results Responses averaged 118±67 words (Fig. 1). Of 20 responses, 17 (85%) were deemed reasonable, 3 (15%) missing important elements/some inaccuracies and none inappropriate or misleading; 16 (80%) emphasized discussion of issues with the healthcare team (Fig. 2). Responses missing important elements/some inaccuracies were those about risks/complications of ablation [missing phrenic nerve palsy, atrioesophageal fistula (AEF), potential need for emergent cardiac surgery or pacemaker, death], concerning symptoms post-procedure (missing symptoms of hematoma, AEF, stroke), and that PFA is not yet approved for use in all regions. Average reading grade level of responses was 13.8 (college level or "professional"): 17 (85%) responses were 12th grade level, 11 (55%) were college-level or higher, and 6 (30%) were college-graduate level ("extremely difficult"). None were ≤ 8th grade level (Fig. 2). Conclusions A majority of ChatGPT responses to common patient questions about AF ablation had reasonable content quality that frequently emphasized the importance of discussion with the healthcare team. However, responses to more difficult questions regarding risks, symptoms of potential complications, or newer technology missed important details; more than half of responses required college-level reading skills. While use of Chat-AI for patient education on EP topics appears promising, patients should be advised to use caution. Further AI training to improve content and readability should be explored.