Artificial intelligence in orthopaedics: can Chat Generative Pre-trained Transformer (ChatGPT) pass Section 1 of the Fellowship of the Royal College of Surgeons (Trauma & Orthopaedics) examination?

Chat Generative Pre-trained Transformer (ChatGPT) is a large language artificial intelligence (AI) model which generates contextually relevant text in response to questioning. After ChatGPT successfully passed the United States Medical Licensing Examinations, proponents have argued it should play an increasing role in medical service provision and education. AI in healthcare remains in its infancy, and the reliability of AI systems must be scrutinized. This study assessed whether ChatGPT could pass Section 1 of the Fellowship of the Royal College of Surgeons (FRCS) examination in Trauma and Orthopaedic Surgery. The UK and Ireland In-Training Examination (UKITE) was used as a surrogate for the FRCS. Papers 1 and 2 of UKITE 2022 were directly inputted into ChatGPT. All questions were in a single-best-answer format without wording alterations. Imaging was trialled to ensure ChatGPT utilized this information. ChatGPT scored 35.8%: 30% lower than the FRCS pass rate and 8.2% lower than the mean score achieved by human candidates of all training levels. Subspecialty analysis demonstrated ChatGPT scored highest in basic science (53.3%) and lowest in trauma (0%). In 87 questions answered incorrectly, ChatGPT only stated it did not know the answer once and gave incorrect explanatory answers for the remaining questions. ChatGPT is currently unable to exert the higher-order judgement and multilogical thinking required to pass the FRCS examination. Further, the current model fails to recognize its own limitations. ChatGPT's deficiencies should be publicized equally as much as its successes to ensure clinicians remain aware of its fallibility.

Cuthbert R ，Simpson AI 《-》

Assessing ChatGPT's ability to pass the FRCS orthopaedic part A exam: A critical analysis.

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.

Saad A ，Iyengar KP ，Kurisunkal V ，Botchu R ... -  《-》

Could ChatGPT Pass the UK Radiology Fellowship Examinations?

Ariyaratne S ，Jenko N ，Mark Davies A ，Iyengar KP ，Botchu R ... -  《-》

Can Artificial Intelligence Pass the American Board of Orthopaedic Surgery Examination? Orthopaedic Residents Versus ChatGPT.

Advances in neural networks, deep learning, and artificial intelligence (AI) have progressed recently. Previous deep learning AI has been structured around domain-specific areas that are trained on dataset-specific areas of interest that yield high accuracy and precision. A new AI model using large language models (LLM) and nonspecific domain areas, ChatGPT (OpenAI), has gained attention. Although AI has demonstrated proficiency in managing vast amounts of data, implementation of that knowledge remains a challenge. (1) What percentage of Orthopaedic In-Training Examination questions can a generative, pretrained transformer chatbot (ChatGPT) answer correctly? (2) How does that percentage compare with results achieved by orthopaedic residents of different levels, and if scoring lower than the 10th percentile relative to 5th-year residents is likely to correspond to a failing American Board of Orthopaedic Surgery score, is this LLM likely to pass the orthopaedic surgery written boards? (3) Does increasing question taxonomy affect the LLM's ability to select the correct answer choices? This study randomly selected 400 of 3840 publicly available questions based on the Orthopaedic In-Training Examination and compared the mean score with that of residents who took the test over a 5-year period. Questions with figures, diagrams, or charts were excluded, including five questions the LLM could not provide an answer for, resulting in 207 questions administered with raw score recorded. The LLM's answer results were compared with the Orthopaedic In-Training Examination ranking of orthopaedic surgery residents. Based on the findings of an earlier study, a pass-fail cutoff was set at the 10th percentile. Questions answered were then categorized based on the Buckwalter taxonomy of recall, which deals with increasingly complex levels of interpretation and application of knowledge; comparison was made of the LLM's performance across taxonomic levels and was analyzed using a chi-square test. ChatGPT selected the correct answer 47% (97 of 207) of the time, and 53% (110 of 207) of the time it answered incorrectly. Based on prior Orthopaedic In-Training Examination testing, the LLM scored in the 40th percentile for postgraduate year (PGY) 1s, the eighth percentile for PGY2s, and the first percentile for PGY3s, PGY4s, and PGY5s; based on the latter finding (and using a predefined cutoff of the 10th percentile of PGY5s as the threshold for a passing score), it seems unlikely that the LLM would pass the written board examination. The LLM's performance decreased as question taxonomy level increased (it answered 54% [54 of 101] of Tax 1 questions correctly, 51% [18 of 35] of Tax 2 questions correctly, and 34% [24 of 71] of Tax 3 questions correctly; p = 0.034). Although this general-domain LLM has a low likelihood of passing the orthopaedic surgery board examination, testing performance and knowledge are comparable to that of a first-year orthopaedic surgery resident. The LLM's ability to provide accurate answers declines with increasing question taxonomy and complexity, indicating a deficiency in implementing knowledge. Current AI appears to perform better at knowledge and interpretation-based inquires, and based on this study and other areas of opportunity, it may become an additional tool for orthopaedic learning and education.

Lum ZC 《-》

Performance of ChatGPT on Solving Orthopedic Board-Style Questions: A Comparative Analysis of ChatGPT 3.5 and ChatGPT 4.

The application of artificial intelligence and large language models in the medical field requires an evaluation of their accuracy in providing medical information. This study aimed to assess the performance of Chat Generative Pre-trained Transformer (ChatGPT) models 3.5 and 4 in solving orthopedic board-style questions. A total of 160 text-only questions from the Orthopedic Surgery Department at Seoul National University Hospital, conforming to the format of the Korean Orthopedic Association board certification examinations, were input into the ChatGPT 3.5 and ChatGPT 4 programs. The questions were divided into 11 subcategories. The accuracy rates of the initial answers provided by Chat GPT 3.5 and ChatGPT 4 were analyzed. In addition, inconsistency rates of answers were evaluated by regenerating the responses. ChatGPT 3.5 answered 37.5% of the questions correctly, while ChatGPT 4 showed an accuracy rate of 60.0% (p < 0.001). ChatGPT 4 demonstrated superior performance across most subcategories, except for the tumor-related questions. The rates of inconsistency in answers were 47.5% for ChatGPT 3.5 and 9.4% for ChatGPT 4. ChatGPT 4 showed the ability to pass orthopedic board-style examinations, outperforming ChatGPT 3.5 in accuracy rate. However, inconsistencies in response generation and instances of incorrect answers with misleading explanations require caution when applying ChatGPT in clinical settings or for educational purposes.

Kim SE ，Lee JH ，Choi BS ，Han HS ，Lee MC ，Ro DH ... -  《-》