BACKGROUND: Artificial Intelligence (AI) large language models (LLM) are tools capable of generating human-like text responses to user queries across topics. The use of these language models in various medical contexts is currently being studied. However, the performance and content quality of these language models have not been evaluated in specific medical fields.
OBJECTIVE: This study aimed to compare the performance of AI LLMs ChatGPT, Gemini and Copilot in providing information to parents about chronic kidney diseases (CKD) and compare the information accuracy and quality with that of a reference source.
METHODS: In this study, 40 frequently asked questions about CKD were identified. The accuracy and quality of the answers were evaluated with reference to the Kidney Disease: Improving Global Outcomes guidelines. The accuracy of the responses generated by LLMs was assessed using F1, precision and recall scores. The quality of the responses was evaluated using a five-point global quality score (GQS).
RESULTS: ChatGPT and Gemini achieved high F1 scores of 0.89 and 1, respectively, in the diagnosis and lifestyle categories, demonstrating significant success in generating accurate responses. Furthermore, ChatGPT and Gemini were successful in generating accurate responses with high precision values in the diagnosis and lifestyle categories. In terms of recall values, all LLMs exhibited strong performance in the diagnosis, treatment and lifestyle categories. Average GQ scores for the responses generated were 3.46 ± 0.55, 1.93 ± 0.63 and 2.02 ± 0.69 for Gemini, ChatGPT 3.5 and Copilot, respectively. In all categories, Gemini performed better than ChatGPT and Copilot.
CONCLUSIONS: Although LLMs provide parents with high-accuracy information about CKD, their use is limited compared with that of a reference source. The limitations in the performance of LLMs can lead to misinformation and potential misinterpretations. Therefore, patients and parents should exercise caution when using these models.

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending widespread adoption and application of Large Language Models (LLMs). However, previous research has predominantly focused on the technical principles of ChatGPT and its social impact, overlooking its effects on human-computer interaction and user psychology. This paper explores the multifaceted impacts of ChatGPT on human-computer interaction, psychology, and society through a literature review. The author investigates ChatGPT\'s technical foundation, including its Transformer architecture and RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms of human-computer interaction, the author studies the significant improvements GPT models bring to conversational interfaces. The analysis extends to psychological impacts, weighing the potential of ChatGPT to mimic human empathy and support learning against the risks of reduced interpersonal connections. In the commercial and social domains, the paper discusses the applications of ChatGPT in customer service and social services, highlighting the improvements in efficiency and challenges such as privacy issues. Finally, the author offers predictions and recommendations for ChatGPT\'s future development directions and its impact on social relationships.

OBJECTIVE: The purpose of this study was to assess how well ChatGPT, an AI-powered chatbot, performed in helping to manage pediatric sialadenitis and identify when sialendoscopy was necessary.
METHODS: 49 clinical cases of pediatric sialadenitis were retrospectively reviewed. ChatGPT was given patient data, and it offered differential diagnoses, proposed further tests, and suggested treatments. The decisions made by the treating otolaryngologists were contrasted with the answers provided by ChatGPT. Analysis was done on ChatGPT response consistency and interrater reliability.
RESULTS: ChatGPT showed 78.57% accuracy in primary diagnosis, and 17.35% of cases were considered likely. On the other hand, otolaryngologists recommended fewer further examinations than ChatGPT (111 vs. 60, p < 0.001). For additional exams, poor agreement was found between ChatGPT and otolaryngologists. Only 28.57% of cases received a pertinent and essential treatment plan via ChatGPT, indicating that the platform\'s treatment recommendations were frequently lacking. For treatment ratings, judges\' interrater reliability was greatest (Kendall\'s tau = 0.824, p < 0.001). For the most part, ChatGPT\'s response constancy was high.
CONCLUSIONS: Although ChatGPT has the potential to correctly diagnose pediatric sialadenitis, there are a number of noteworthy limitations with regard to its ability to suggest further testing and treatment regimens. Before widespread clinical use, more research and confirmation are required. To guarantee that chatbots are utilized properly and effectively to supplement human expertise rather than to replace it, a critical viewpoint is required.

OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.

BACKGROUND: ChatGPT is an AI platform whose relevance in the peer review of scientific articles is steadily growing. Nonetheless, it has sparked debates over its potential biases and inaccuracies. This study aims to assess ChatGPT\'s ability to qualitatively emulate human reviewers in scientific research.
METHODS: We included the first submitted version of the latest twenty original research articles published by the 3rd of July 2023, in a high-profile medical journal. Each article underwent evaluation by a minimum of three human reviewers during the initial review stage. Subsequently, three researchers with medical backgrounds and expertise in manuscript revision, independently and qualitatively assessed the agreement between the peer reviews generated by ChatGPT version GPT-4 and the comments provided by human reviewers for these articles. The level of agreement was categorized into complete, partial, none, or contradictory.
RESULTS: 720 human reviewers\' comments were assessed. There was a good agreement between the three assessors (Overall kappa >0.6). ChatGPT\'s comments demonstrated complete agreement in terms of quality and substance with 48 (6.7 %) human reviewers\' comments, partially agreed with 92 (12.8 %), identifying issues necessitating further elaboration or recommending supplementary steps to address concerns, had no agreement with a significant 565 (78.5 %), and contradicted 15 (2.1 %). ChatGPT comments on methods had the lowest proportion of complete agreement (13 comments, 3.6 %), while general comments on the manuscript displayed the highest proportion of complete agreement (17 comments, 22.1 %).
CONCLUSIONS: ChatGPT version GPT-4 has a limited ability to emulate human reviewers within the peer review process of scientific research.

BACKGROUND: In the era of the internet, individuals have increasingly accustomed themselves to gathering necessary information and expressing their opinions on public web-based platforms. The health care sector is no exception, as these comments, to a certain extent, influence people\'s health care decisions. During the onset of the COVID-19 pandemic, how the medical experience of Chinese patients and their evaluations of hospitals have changed remains to be studied. Therefore, we plan to collect patient medical visit data from the internet to reflect the current status of medical relationships under specific circumstances.
OBJECTIVE: This study aims to explore the differences in patient comments across various stages (during, before, and after) of the COVID-19 pandemic, as well as among different types of hospitals (children\'s hospitals, maternity hospitals, and tumor hospitals). Additionally, by leveraging ChatGPT (OpenAI), the study categorizes the elements of negative hospital evaluations. An analysis is conducted on the acquired data, and potential solutions that could improve patient satisfaction are proposed. This study is intended to assist hospital managers in providing a better experience for patients who are seeking care amid an emergent public health crisis.
METHODS: Selecting the top 50 comprehensive hospitals nationwide and the top specialized hospitals (children\'s hospitals, tumor hospitals, and maternity hospitals), we collected patient reviews from these hospitals on the Dianping website. Using ChatGPT, we classified the content of negative reviews. Additionally, we conducted statistical analysis using SPSS (IBM Corp) to examine the scoring and composition of negative evaluations.
RESULTS: A total of 30,317 pieces of effective comment information were collected from January 1, 2018, to August 15, 2023, including 7696 pieces of negative comment information. Manual inspection results indicated that ChatGPT had an accuracy rate of 92.05%. The F1-score was 0.914. The analysis of this data revealed a significant correlation between the comments and ratings received by hospitals during the pandemic. Overall, there was a significant increase in average comment scores during the outbreak (P<.001). Furthermore, there were notable differences in the composition of negative comments among different types of hospitals (P<.001). Children\'s hospitals received sensitive feedback regarding waiting times and treatment effectiveness, while patients at maternity hospitals showed a greater concern for the attitude of health care providers. Patients at tumor hospitals expressed a desire for timely examinations and treatments, especially during the pandemic period.
CONCLUSIONS: The COVID-19 pandemic had some association with patient comment scores. There were variations in the scores and content of comments among different types of specialized hospitals. Using ChatGPT to analyze patient comment content represents an innovative approach for statistically assessing factors contributing to patient dissatisfaction. The findings of this study could provide valuable insights for hospital administrators to foster more harmonious physician-patient relationships and enhance hospital performance during public health emergencies.

BACKGROUND: Artificial intelligence chatbot tools responses might discern patterns and correlations that may elude human observation, leading to more accurate and timely interventions. However, their reliability to answer healthcare-related questions is still debated. This study aimed to assess the performance of the three versions of GPT-based chatbots about prosthetic joint infections (PJI).
METHODS: Thirty questions concerning the diagnosis and treatment of hip and knee PJIs, stratified by a priori established difficulty, were generated by a team of experts, and administered to ChatGPT 3.5, BingChat, and ChatGPT 4.0. Responses were rated by three orthopedic surgeons and two infectious diseases physicians using a five-point Likert-like scale with numerical values to quantify the quality of responses. Inter-rater reliability was assessed by interclass correlation statistics.
RESULTS: Responses averaged \"good-to-very good\" for all chatbots examined, both in diagnosis and treatment, with no significant differences according to the difficulty of the questions. However, BingChat ratings were significantly lower in the treatment setting (p = 0.025), particularly in terms of accuracy (p = 0.02) and completeness (p = 0.004). Agreement in ratings among examiners appeared to be very poor.
CONCLUSIONS: On average, the quality of responses is rated positively by experts, but with ratings that frequently may vary widely. This currently suggests that AI chatbot tools are still unreliable in the management of PJI.

OBJECTIVE: Large language models (LLMs) are rapidly advancing and demonstrating high performance in understanding textual information, suggesting potential applications in interpreting patient histories and documented imaging findings. As LLMs continue to improve, their diagnostic abilities are expected to be enhanced further. However, there is a lack of comprehensive comparisons between LLMs from different manufacturers. In this study, we aimed to test the diagnostic performance of the three latest major LLMs (GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro) using Radiology Diagnosis Please Cases, a monthly diagnostic quiz series for radiology experts.
METHODS: Clinical history and imaging findings, provided textually by the case submitters, were extracted from 324 quiz questions originating from Radiology Diagnosis Please cases published between 1998 and 2023. The top three differential diagnoses were generated by GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, using their respective application programming interfaces. A comparative analysis of diagnostic performance among these three LLMs was conducted using Cochrane\'s Q and post hoc McNemar\'s tests.
RESULTS: The respective diagnostic accuracies of GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro for primary diagnosis were 41.0%, 54.0%, and 33.9%, which further improved to 49.4%, 62.0%, and 41.0%, when considering the accuracy of any of the top three differential diagnoses. Significant differences in the diagnostic performance were observed among all pairs of models.
CONCLUSIONS: Claude 3 Opus outperformed GPT-4o and Gemini 1.5 Pro in solving radiology quiz cases. These models appear capable of assisting radiologists when supplied with accurate evaluations and worded descriptions of imaging findings.

OBJECTIVE: This study compared three artificial intelligence (AI) platforms\' potential to identify drug therapy communication competencies expected of a graduating medical doctor.
METHODS: We presented three AI platforms, namely, Poe Assistant©, ChatGPT© and Google Bard©, with structured queries to generate communication skill competencies and case scenarios appropriate for graduating medical doctors. These case scenarios comprised 15 prototypical medical conditions that required drug prescriptions. Two authors independently evaluated the AI-enhanced clinical encounters, which integrated a diverse range of information to create patient-centred care plans. Through a consensus-based approach using a checklist, the communication components generated for each scenario were assessed. The instructions and warnings provided for each case scenario were evaluated by referencing the British National Formulary.
RESULTS: AI platforms demonstrated overlap in competency domains generated, albeit with variations in wording. The domains of knowledge (basic and clinical pharmacology, prescribing, communication and drug safety) were unanimously recognized by all platforms. A broad consensus among Poe Assistant© and ChatGPT© on drug therapy-related communication issues specific to each case scenario was evident. The consensus primarily encompassed salutation, generic drug prescribed, treatment goals and follow-up schedules. Differences were observed in patient instruction clarity, listed side effects, warnings and patient empowerment. Google Bard did not provide guidance on patient communication issues.
CONCLUSIONS: AI platforms recognized competencies with variations in how these were stated. Poe Assistant© and ChatGPT© exhibited alignment of communication issues. However, significant discrepancies were observed in specific skill components, indicating the necessity of human intervention to critically evaluate AI-generated outputs.

Background There has been an explosion of commentary and discussion about the ethics and utility of using artificial intelligence in medicine, and its practical use in medical education is still being debated. Through qualitative research methods, this study aims to highlight the advantages and pitfalls of using ChatGPT in the development of clinical reasoning cases for medical student education. Methods Five highly experienced faculty in medical education were provided instructions to create unique clinical reasoning cases for three different chief concerns using ChatGPT 3.0. Faculty were then asked to reflect on and review the created cases. Finally, a focus group was conducted to further analyze and describe their experiences with the new technology. Results Overall, faculty found the use of ChatGPT in the development of clinical reasoning cases easy to use but difficult to get to certain objectives and largely incapable of being creative enough to create complexity for student use without heavy editing. The created cases did provide a helpful starting point and were extremely efficient; however, faculty did experience some medical inaccuracies and fact fabrication. Conclusion There is value to using ChatGPT to develop curricular content, especially for clinical reasoning cases, but it needs to be comprehensively reviewed and verified. To efficiently and effectively utilize the tool, educators will need to develop a framework that can be easily translatable into simple prompts that ChatGPT can understand. Future work will need to strongly consider the risks of recirculating biases and misinformation.