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Purpose: This study analyses the performance and proficiency of the three Artificial Intelligence (AI) generative chatbots (ChatGPT-3.5, ChatGPT-4.0, Bard Google AI®) and in answering the Multiple Choice Questions (MCQs) of postgraduate (PG) level orthopaedic qualifying examinations., Methods: A series of 120 mock Single Best Answer' (SBA) MCQs with four possible options named A, B, C and D as answers on various musculoskeletal (MSK) conditions covering Trauma and Orthopaedic curricula were compiled. A standardised text prompt was used to generate and feed ChatGPT (both 3.5 and 4.0 versions) and Google Bard programs, which were then statistically analysed., Results: Significant differences were found between responses from Chat GPT 3.5 with Chat GPT 4.0 (Chi square = 27.2, P < 0.001) and on comparing both Chat GPT 3.5 (Chi square = 63.852, P < 0.001) with Chat GPT 4.0 (Chi square = 44.246, P < 0.001) with. Bard Google AI® had 100% efficiency and was significantly more efficient than both Chat GPT 3.5 with Chat GPT 4.0 (p < 0.0001)., Conclusion: The results demonstrate the variable potential of the different AI generative chatbots (Chat GPT 3.5, Chat GPT 4.0 and Bard Google) in their ability to answer the MCQ of PG-level orthopaedic qualifying examinations. Bard Google AI® has shown superior performance than both ChatGPT versions, underlining the potential of such large language processing models in processing and applying orthopaedic subspecialty knowledge at a PG level., (© 2024. The Author(s) under exclusive licence to SICOT aisbl.)

Rationale and Objectives: Chat Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence (AI) tool which utilises machine learning to generate original text resembling human language. AI models have recently demonstrated remarkable ability at analysing and solving problems, including passing professional examinations. We investigate the performance of ChatGPT on some of the UK radiology fellowship equivalent examination questions., Methods: ChatGPT was asked to answer questions from question banks resembling the Fellowship of the Royal College of Radiologists (FRCR) examination. The entire physics part 1 question bank (203 5-part true/false questions) was answered by the GPT-4 model and answers recorded. 240 single best answer questions (SBAs) (representing the true length of the FRCR 2A examination) were answered by both GPT-3.5 and GPT-4 models., Results: ChatGPT 4 answered 74.8% of part 1 true/false statements correctly. The spring 2023 passing mark of the part 1 examination was 75.5% and ChatGPT thus narrowly failed. In the 2A examination, ChatGPT 3.5 answered 50.8% SBAs correctly, while GPT-4 answered 74.2% correctly. The winter 2022 2A pass mark was 63.3% and thus GPT-4 clearly passed., Conclusion: AI models such as ChatGPT are able to answer the majority of questions in an FRCR style examination. It is reasonable to assume that further developments in AI will be more likely to succeed in comprehending and solving questions related to medicine, specifically clinical radiology., Advances in Knowledge: Our findings outline the unprecedented capabilities of AI, adding to the current relatively small body of literature on the subject, which in turn can play a role medical training, evaluation and practice. This can undoubtedly have implications for radiology., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Peer review is the established method for evaluating the quality and validity of research manuscripts in scholarly publishing. However, scientific peer review faces challenges as the volume of submitted research has steadily increased in recent years. Time constraints and peer review quality assurance can place burdens on reviewers, potentially discouraging their participation. Some artificial intelligence (AI) tools might assist in relieving these pressures. This study explores the efficiency and effectiveness of one of the artificial intelligence (AI) chatbots, ChatGPT (Generative Pre-trained Transformer), in the peer review process., Methods: Twenty-one peer-reviewed research articles were anonymised to ensure unbiased evaluation. Each article was reviewed by two humans and by versions 3.5 and 4.0 of ChatGPT. The AI was instructed to provide three positive and three negative comments on the articles and recommend whether they should be accepted or rejected. The human and AI results were compared using a 5-point Likert scale to determine the level of agreement. The correlation between ChatGPT responses and the acceptance or rejection of the papers was also examined., Results: Subjective review similarity between human reviewers and ChatGPT showed a mean score of 3.6/5 for ChatGPT 3.5 and 3.76/5 for ChatGPT 4.0. The correlation between human and AI review scores was statistically significant for ChatGPT 3.5, but not for ChatGPT 4.0., Conclusion: ChatGPT can complement human scientific peer review, enhancing efficiency and promptness in the editorial process. However, a fully automated AI review process is currently not advisable, and ChatGPT's role should be regarded as highly constrained for the present and near future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Research Trust of DiabetesIndia (DiabetesIndia) and National Diabetes Obesity and Cholesterol Foundation (N-DOC). Published by Elsevier Ltd. All rights reserved.)

Objective: ChatGPT (Generative Pre-trained Transformer) is an artificial intelligence language tool developed by OpenAI that utilises machine learning algorithms to generate text that closely mimics human language. It has recently taken the internet by storm. There have been several concerns regarding the accuracy of documents it generates. This study compares the accuracy and quality of several ChatGPT-generated academic articles with those written by human authors., Material and Methods: We performed a study to assess the accuracy of ChatGPT-generated radiology articles by comparing them with the published or written, and under review articles. These were independently analysed by two fellowship-trained musculoskeletal radiologists and graded from 1 to 5 (1 being bad and inaccurate to 5 being excellent and accurate)., Results: In total, 4 of the 5 articles written by ChatGPT were significantly inaccurate with fictitious references. One of the papers was well written, with a good introduction and discussion; however, all references were fictitious., Conclusion: ChatGPT is able to generate coherent research articles, which on initial review may closely resemble authentic articles published by academic researchers. However, all of the articles we assessed were factually inaccurate and had fictitious references. It is worth noting, however, that the articles generated may appear authentic to an untrained reader., (© 2023. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

The exact prompts provided to ChatGPT in order to generate each of the articles are mentioned in Table 1 of our article under "Question" [[1]]. Effective governance, monitoring and establishing stringent guidelines and frameworks, is paramount to ensure that the capabilities of AI language tools are appropriately leveraged in academic research, and also to explore the ethical and legal implications and potential biases. AI language models nonetheless have the potential to serve as a valuable adjunctive tool in research, and collaborative publishing by humans with the help of AI tools may drive academic writing in the future [[3]]. [Extracted from the article]

Chat Generative Pre-trained Transformer (ChatGPT); an open access artificial intelligence (AI) tool has been in the limelight with its ability to respond to prompts, analyse data information using algorithms to augment efficiency in day-to-day activities across a spectrum of human activities including MSK/Orthopaedic science. The purpose of this cross-sectional survey has been to analyse the knowledge, understanding of the role of Chat Generative Pre-trained Transformer (ChatGPT) and its implications in clinical practice as well as research in medicine. An online cross-sectional survey of 10 questions (multiple choice and free text) was circulated amongst orthopaedic surgeons, musculoskeletal radiologists and Rheumatologists in India and UK, to evaluate perception of Chat Generative Pre-trained Transformer (ChatGPT) AI Tool. We had 125 responses with majority being aware of ChatGPT though a minority had used it. There was consensus that its going have detrimental effect on workforce with majority of the opinion that they would be used to create radiology reports. Mixed responses were noted regarding the quality of research and role of ChatGPT being an anonymous author. There is a considerable debate amongst clinicians of orthopaedic, radiology and rheumatology -specialities. The attitudes are mixed but mainly positive, although there are many concerns about the still-evolving new technology. Diagnostic Study level 4. [ABSTRACT FROM AUTHOR]

Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room. Musculoskeletal trauma results in expenditure of billions of dollars and protracted losses of quality-adjusted life years. New and innovative methods are needed to minimise the impact by ensuring quick and accurate assessment. However, each of the currently utilised radiological procedures, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging, has resulted in implosion of medical imaging data. Deep learning, a recent advancement in artificial intelligence, has demonstrated the potential to analyse medical images with sensitivity and specificity at par with experts. In this review article, we intend to summarise and showcase the various developments which have occurred in the dynamic field of artificial intelligence and machine learning and how their applicability to different aspects of imaging in trauma can be explored to improvise our existing reporting systems and improvise on patient outcomes. [ABSTRACT FROM AUTHOR]

AI technology has made significant advancements in recent years, with the notable development of ChatGPT in November 2022. Users have observed evidence of deductive reasoning, logical thinking, and coherent thought in ChatGPT's responses. This study aimed to determine if ChatGPT has the capability to pass the Orthopaedic Fellow of the Royal College of Surgeons (FRCS Orth) Part A exam. To assess ChatGPT4's ability to pass the Orthopaedic FRCS Orth Part A exam, a study was conducted using 240 mock FRCS Orth Part A questions. The study evaluated the accuracy of ChatGPT's answers and the response time for each question. Descriptive statistics were employed to analyse the chatbot's performance. The evaluation revealed that ChatGPT4 achieved an overall score of 67.5% on Part A of the exam. However, ChatGPT4 did not meet the overall pass mark required for the FRCS Orth Part A exam. This study demonstrates that ChatGPT was unable to pass the FRCS Orthopaedic examination. Several factors contributed to this outcome, including the lack of critical or high-order thinking abilities, limited clinical expertise, and the inability to meet the rigorous requirements of the exam. • AI technology has made significant advancements in recent years • ChatGPT4 achieved an overall score of 67.5% on Part A of the exam • Chat GPT lacks critical or high-order thinking abilities and clinical expertise [ABSTRACT FROM AUTHOR]