Background: ChatGPT is a natural language processing model developed by OpenAI, which can be iteratively updated and optimized to accommodate the changing and complex requirements of human verbal communication. Objective: The study aimed to evaluate ChatGPT’s accuracy in answering orthopedics-related multiple-choice questions (MCQs) and assess its short-term effects as a learning aid through a randomized controlled trial. In addition, long-term effects on student performance in other subjects were measured using final examination results. Methods: We first evaluated ChatGPT’s accuracy in answering MCQs pertaining to orthopedics across various question formats. Then, 129 undergraduate medical students participated in a randomized controlled study in which the ChatGPT group used ChatGPT as a learning tool, while the control group was prohibited from using artificial intelligence software to support learning. Following a 2-week intervention, the 2 groups’ understanding of orthopedics was assessed by an orthopedics test, and variations in the 2 groups’ performance in other disciplines were noted through a follow-up at the end of the semester. Results: ChatGPT-4.0 answered 1051 orthopedics-related MCQs with a 70.60% (742/1051) accuracy rate, including 71.8% (237/330) accuracy for A1 MCQs, 73.7% (330/448) accuracy for A2 MCQs, 70.2% (92/131) accuracy for A3/4 MCQs, and 58.5% (83/142) accuracy for case analysis MCQs. As of April 7, 2023, a total of 129 individuals participated in the experiment. However, 19 individuals withdrew from the experiment at various phases; thus, as of July 1, 2023, a total of 110 individuals accomplished the trial and completed all follow-up work. After we intervened in the learning style of the students in the short term, the ChatGPT group answered more questions correctly than the control group (ChatGPT group: mean 141.20, SD 26.68; control group: mean 130.80, SD 25.56; P=.04) in the orthopedics test, particularly on A1 (ChatGPT group: mean 46.57, SD 8.52; control group: mean 42.18, SD 9.43; P=.01), A2 (ChatGPT group: mean 60.59, SD 10.58; control group: mean 56.66, SD 9.91; P=.047), and A3/4 MCQs (ChatGPT group: mean 19.57, SD 5.48; control group: mean 16.46, SD 4.58; P=.002). At the end of the semester, we found that the ChatGPT group performed better on final examinations in surgery (ChatGPT group: mean 76.54, SD 9.79; control group: mean 72.54, SD 8.11; P=.02) and obstetrics and gynecology (ChatGPT group: mean 75.98, SD 8.94; control group: mean 72.54, SD 8.66; P=.04) than the control group. Conclusions: ChatGPT answers orthopedics-related MCQs accurately, and students using it excel in both short-term and long-term assessments. Our findings strongly support ChatGPT’s integration into medical education, enhancing contemporary instructional methods. Trial Registration: Chinese Clinical Trial Registry Chictr2300071774; https://www.chictr.org.cn/hvshowproject.html ?id=225740&v=1.0

Background: The creation of large language models (LLMs) such as ChatGPT is an important step in the development of artificial intelligence, which shows great potential in medical education due to its powerful language understanding and generative capabilities. The purpose of this study was to quantitatively evaluate and comprehensively analyze ChatGPT’s performance in handling questions for the National Nursing Licensure Examination (NNLE) in China and the United States, including the National Council Licensure Examination for Registered Nurses (NCLEX-RN) and the NNLE. Objective: This study aims to examine how well LLMs respond to the NCLEX-RN and the NNLE multiple-choice questions (MCQs) in various language inputs. To evaluate whether LLMs can be used as multilingual learning assistance for nursing, and to assess whether they possess a repository of professional knowledge applicable to clinical nursing practice. Methods: First, we compiled 150 NCLEX-RN Practical MCQs, 240 NNLE Theoretical MCQs, and 240 NNLE Practical MCQs. Then, the translation function of ChatGPT 3.5 was used to translate NCLEX-RN questions from English to Chinese and NNLE questions from Chinese to English. Finally, the original version and the translated version of the MCQs were inputted into ChatGPT 4.0, ChatGPT 3.5, and Google Bard. Different LLMs were compared according to the accuracy rate, and the differences between different language inputs were compared. Results: The accuracy rates of ChatGPT 4.0 for NCLEX-RN practical questions and Chinese-translated NCLEX-RN practical questions were 88.7% (133/150) and 79.3% (119/150), respectively. Despite the statistical significance of the difference (P=.03), the correct rate was generally satisfactory. Around 71.9% (169/235) of NNLE Theoretical MCQs and 69.1% (161/233) of NNLE Practical MCQs were correctly answered by ChatGPT 4.0. The accuracy of ChatGPT 4.0 in processing NNLE Theoretical MCQs and NNLE Practical MCQs translated into English was 71.5% (168/235; P=.92) and 67.8% (158/233; P=.77), respectively, and there was no statistically significant difference between the results of text input in different languages. ChatGPT 3.5 (NCLEX-RN P=.003, NNLE Theoretical P<.001, NNLE Practical P=.12) and Google Bard (NCLEX-RN P<.001, NNLE Theoretical P<.001, NNLE Practical P<.001) had lower accuracy rates for nursing-related MCQs than ChatGPT 4.0 in English input. English accuracy was higher when compared with ChatGPT 3.5’s Chinese input, and the difference was statistically significant (NCLEX-RN P=.02, NNLE Practical P=.02). Whether submitted in Chinese or English, the MCQs from the NCLEX-RN and NNLE demonstrated that ChatGPT 4.0 had the highest number of unique correct responses and the lowest number of unique incorrect responses among the 3 LLMs. Conclusions: This study, focusing on 618 nursing MCQs including NCLEX-RN and NNLE exams, found that ChatGPT 4.0 outperformed ChatGPT 3.5 and Google Bard in accuracy. It excelled in processing English and Chinese inputs, underscoring its potential as a valuable tool in nursing education and clinical decision-making.

Background: Recent advancements in artificial intelligence (AI) like ChatGPT have expanded possibilities for patient education, yet its impact on perioperative anxiety in total knee arthroplasty (TKA) patients remains unexplored. Methods: In this single-blind, randomized controlled pilot study from April to July 2023, 60 patients were randomly allocated using sealed envelopes to either ChatGPT-assisted or traditional surgeon-led informed consent groups. In the ChatGPT group, physicians used ChatGPT 4.0 to provide standardized, comprehensive responses to patient queries during the consent process, while maintaining their role in interpreting and contextualizing the information. Outcomes were measured using Hospital Anxiety and Depression Scales (HADS), Perioperative Apprehension Scale-7 (PAS-7), Visual Analogue Scales for Anxiety and Pain (VAS-A, VAS-P), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and satisfaction questionnaires. Results: Of 55 patients completing the study, the ChatGPT group showed significantly lower anxiety scores after informed consent (HADS-A: 10.48 ± 3.84 vs 12.75 ± 4.12, P = .04, Power = .67; PAS-7: 12.44 ± 3.70 vs 14.64 ± 2.11, P = .01, Power = .85; VAS-A: 5.40 ± 1.89 vs 6.71 ± 2.27, P = .02, Power = .75) and on the fifth postoperative day (HADS-A: 8.33 ± 3.20 vs 10.71 ± 3.83, P = .01, Power = .79; VAS-A: 3.41 ± 1.58 vs 4.64 ± 1.70, P = .008, Power = .85). The ChatGPT group also reported higher satisfaction with preoperative education (4.22 ± 0.51 vs 3.43 ± 0.84, P<.001, Power = .99) and overall hospitalization experience (4.11 ± 0.65 vs 3.46 ± 0.69, P = .001, Power = .97). No significant differences were found in depression scores, knee function, or pain levels. Conclusions: ChatGPT-assisted informed consent effectively reduced perioperative anxiety and improved patient satisfaction in TKA patients. While these preliminary findings are promising, larger studies are needed to validate these results and explore broader applications of AI in preoperative patient education.

Background: The widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China’s DingXiangYuan to offer remote medical services. Objective: This study evaluates ChatGPT-4’s responses against those of professional health care providers in telemedicine, assessing artificial intelligence’s capability to support the surge in remote medical consultations and its impact on health care delivery. Methods: We sourced remote orthopedic consultations from “Doctor DingXiang,” with responses from its certified physicians as the control and ChatGPT’s responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: “logical reasoning,” “internal information,” “external information,” “guiding function,” “therapeutic effect,” “medical knowledge popularization education,” and “overall satisfaction.” We used Fleiss κ to measure agreement among multiple raters. Results: Initially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT’s “internal information” score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; P=.43) and “therapeutic effect” score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; P=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher “logical reasoning” score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; P=.38), “external information” score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; P=.25), and “guiding function” score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; P=.96), although the differences were not statistically significant. Meanwhile, the “medical knowledge popularization education” score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; P<.001), and the difference was statistically significant. In terms of “overall satisfaction,” the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; P=.92). According to how Fleiss κ values were interpreted, 6 of the control group’s score points were classified as displaying “fair agreement” (P<.001), and 1 was classified as showing “substantial agreement” (P<.001). In the experimental group, 3 points were classified as indicating “fair agreement,” while 4 suggested “moderate agreement” (P<.001). Conclusions: ChatGPT-4 matches the expertise found in DingXiangYuan forums’ paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries.

Background: Large language models (LLMs) are revolutionizing natural language processing, increasingly applied in clinical settings to enhance preoperative patient education. Objective: This study aimed to evaluate the effectiveness and applicability of various LLMs in preoperative patient education by analyzing their responses to superior capsular reconstruction (SCR)–related inquiries. Methods: In total, 10 sports medicine clinical experts formulated 11 SCR issues and developed preoperative patient education strategies during a webinar, inputting 12 text commands into Claude-3-Opus (Anthropic), GPT-4-Turbo (OpenAI), and Gemini-1.5-Pro (Google DeepMind). A total of 3 experts assessed the language models’ responses for correctness, completeness, logic, potential harm, and overall satisfaction, while preoperative education documents were evaluated using DISCERN questionnaire and Patient Education Materials Assessment Tool instruments, and reviewed by 5 postoperative patients for readability and educational value; readability of all responses was also analyzed using the cntext package and py-readability-metrics. Results: Between July 1 and August 17, 2024, sports medicine experts and patients evaluated 33 responses and 3 preoperative patient education documents generated by 3 language models regarding SCR surgery. For the 11 query responses, clinicians rated Gemini significantly higher than Claude in all categories (P<.05) and higher than GPT in completeness, risk avoidance, and overall rating (P<.05). For the 3 educational documents, Gemini’s Patient Education Materials Assessment Tool score significantly exceeded Claude’s (P=.03), and patients rated Gemini’s materials superior in all aspects, with significant differences in educational quality versus Claude (P=.02) and overall satisfaction versus both Claude (P<.01) and GPT (P=.01). GPT had significantly higher readability than Claude on 3 R-based metrics (P<.01). Interrater agreement was high among clinicians and fair among patients. Conclusions: Claude-3-Opus, GPT-4-Turbo, and Gemini-1.5-Pro effectively generated readable presurgical education materials but lacked citations and failed to discuss alternative treatments or the risks of forgoing SCR surgery, highlighting the need for expert oversight when using these LLMs in patient education.

Objective: The meta-analysis aimed to integrate the evidence of randomized control trials to estimate the efficacy of prophylactic tamsulosin on postoperative urinary retention (POUR). Methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched through 1 March 2022 using predetermined keywords. Randomized control trials reporting the preventive efficacy of prophylactic tamsulosin against POUR were identified according to the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guideline. Pooled risk ratios (RRs) were calculated using a random-effects model or a fixed-effects model based on the results of heterogeneity assessment. A meta-regression analysis was performed to explore the potential sources of heterogeneity. Results: There were 14 studies with 1102 patients in the Tamsulosin group and 1119 patients in the Control group. The risk of POUR was significantly lower in the Tamsulosin group (156/1102 [14.2%] vs. 238/1119 [21.3%]; RR=0.65; 95% CI: 0.50–0.86; P=0.002; Heterogeneity: I ²=51%; P=0.01). Tamsulosin administration was associated with a higher risk of adverse events (65/614 [10.6%] vs. 39/626 [6.2%]; RR=1.72; 95% CI: 1.19–2.48; P=0.004; Heterogeneity: I ²=0%; P=0.70). The meta-regression identified the mean age of patients as the only potential source of heterogeneity. Subgroup analysis showed that the younger patients (age <50 years) might benefit more from tamsulosin intake (RR=0.36; 95% CI: 0.19–0.70; P=0.003; Heterogeneity: I ²=49%; P=0.14). Conclusions: The current meta-analysis suggested that prophylactic tamsulosin contributed to the prevention of POUR, and younger patients (<50 years) might benefit more from this preventive regimen. Tamsulosin was also associated with a higher risk of adverse events.