Comparing the Performance of Popular Large Language Models on the National Board of Medical Examiners Sample Questions.

Large language models (LLMs) have transformed various domains in medicine, aiding in complex tasks and clinical decision-making, with OpenAI's GPT-4, GPT-3.5, Google's Bard, and Anthropic's Claude among the most widely used. While GPT-4 has demonstrated superior performance in some studies, comprehensive comparisons among these models remain limited. Recognizing the significance of the National Board of Medical Examiners (NBME) exams in assessing the clinical knowledge of medical students, this study aims to compare the accuracy of popular LLMs on NBME clinical subject exam sample questions. The questions used in this study were multiple-choice questions obtained from the official NBME website and are publicly available. Questions from the NBME subject exams in medicine, pediatrics, obstetrics and gynecology, clinical neurology, ambulatory care, family medicine, psychiatry, and surgery were used to query each LLM. The responses from GPT-4, GPT-3.5, Claude, and Bard were collected in October 2023. The response by each LLM was compared to the answer provided by the NBME and checked for accuracy. Statistical analysis was performed using one-way analysis of variance (ANOVA). A total of 163 questions were queried by each LLM. GPT-4 scored 163/163 (100%), GPT-3.5 scored 134/163 (82.2%), Bard scored 123/163 (75.5%), and Claude scored 138/163 (84.7%). The total performance of GPT-4 was statistically superior to that of GPT-3.5, Claude, and Bard by 17.8%, 15.3%, and 24.5%, respectively. The total performance of GPT-3.5, Claude, and Bard was not significantly different. GPT-4 significantly outperformed Bard in specific subjects, including medicine, pediatrics, family medicine, and ambulatory care, and GPT-3.5 in ambulatory care and family medicine. Across all LLMs, the surgery exam had the highest average score (18.25/20), while the family medicine exam had the lowest average score (3.75/5).  Conclusion: GPT-4's superior performance on NBME clinical subject exam sample questions underscores its potential in medical education and practice. While LLMs exhibit promise, discernment in their application is crucial, considering occasional inaccuracies. As technological advancements continue, regular reassessments and refinements are imperative to maintain their reliability and relevance in medicine.

Abbas A ，Rehman MS ，Rehman SS 《Cureus》

Artificial Intelligence for Anesthesiology Board-Style Examination Questions: Role of Large Language Models.

New artificial intelligence tools have been developed that have implications for medical usage. Large language models (LLMs), such as the widely used ChatGPT developed by OpenAI, have not been explored in the context of anesthesiology education. Understanding the reliability of various publicly available LLMs for medical specialties could offer insight into their understanding of the physiology, pharmacology, and practical applications of anesthesiology. An exploratory prospective review was conducted using 3 commercially available LLMs--OpenAI's ChatGPT GPT-3.5 version (GPT-3.5), OpenAI's ChatGPT GPT-4 (GPT-4), and Google's Bard--on questions from a widely used anesthesia board examination review book. Of the 884 eligible questions, the overall correct answer rates were 47.9% for GPT-3.5, 69.4% for GPT-4, and 45.2% for Bard. GPT-4 exhibited significantly higher performance than both GPT-3.5 and Bard (p = 0.001 and p < 0.001, respectively). None of the LLMs met the criteria required to secure American Board of Anesthesiology certification, according to the 70% passing score approximation. GPT-4 significantly outperformed GPT-3.5 and Bard in terms of overall performance, but lacked consistency in providing explanations that aligned with scientific and medical consensus. Although GPT-4 shows promise, current LLMs are not sufficiently advanced to answer anesthesiology board examination questions with passing success. Further iterations and domain-specific training may enhance their utility in medical education.

Khan AA ，Yunus R ，Sohail M ，Rehman TA ，Saeed S ，Bu Y ，Jackson CD ，Sharkey A ，Mahmood F ，Matyal R ... -  《-》

Evaluating Large Language Models for the National Premedical Exam in India: Comparative Analysis of GPT-3.5, GPT-4, and Bard.

Large language models (LLMs) have revolutionized natural language processing with their ability to generate human-like text through extensive training on large data sets. These models, including Generative Pre-trained Transformers (GPT)-3.5 (OpenAI), GPT-4 (OpenAI), and Bard (Google LLC), find applications beyond natural language processing, attracting interest from academia and industry. Students are actively leveraging LLMs to enhance learning experiences and prepare for high-stakes exams, such as the National Eligibility cum Entrance Test (NEET) in India. This comparative analysis aims to evaluate the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. In this paper, we evaluated the performance of the 3 mainstream LLMs, namely GPT-3.5, GPT-4, and Google Bard, in answering questions related to the NEET-2023 exam. The questions of the NEET were provided to these artificial intelligence models, and the responses were recorded and compared against the correct answers from the official answer key. Consensus was used to evaluate the performance of all 3 models. It was evident that GPT-4 passed the entrance test with flying colors (300/700, 42.9%), showcasing exceptional performance. On the other hand, GPT-3.5 managed to meet the qualifying criteria, but with a substantially lower score (145/700, 20.7%). However, Bard (115/700, 16.4%) failed to meet the qualifying criteria and did not pass the test. GPT-4 demonstrated consistent superiority over Bard and GPT-3.5 in all 3 subjects. Specifically, GPT-4 achieved accuracy rates of 73% (29/40) in physics, 44% (16/36) in chemistry, and 51% (50/99) in biology. Conversely, GPT-3.5 attained an accuracy rate of 45% (18/40) in physics, 33% (13/26) in chemistry, and 34% (34/99) in biology. The accuracy consensus metric showed that the matching responses between GPT-4 and Bard, as well as GPT-4 and GPT-3.5, had higher incidences of being correct, at 0.56 and 0.57, respectively, compared to the matching responses between Bard and GPT-3.5, which stood at 0.42. When all 3 models were considered together, their matching responses reached the highest accuracy consensus of 0.59. The study's findings provide valuable insights into the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. GPT-4 emerged as the most accurate model, highlighting its potential for educational applications. Cross-checking responses across models may result in confusion as the compared models (as duos or a trio) tend to agree on only a little over half of the correct responses. Using GPT-4 as one of the compared models will result in higher accuracy consensus. The results underscore the suitability of LLMs for high-stakes exams and their positive impact on education. Additionally, the study establishes a benchmark for evaluating and enhancing LLMs' performance in educational tasks, promoting responsible and informed use of these models in diverse learning environments.

Farhat F ，Chaudhry BM ，Nadeem M ，Sohail SS ，Madsen DØ ... -  《-》

Evidence-based potential of generative artificial intelligence large language models in orthodontics: a comparative study of ChatGPT, Google Bard, and Microsoft Bing.

The increasing utilization of large language models (LLMs) in Generative Artificial Intelligence across various medical and dental fields, and specifically orthodontics, raises questions about their accuracy. This study aimed to assess and compare the answers offered by four LLMs: Google's Bard, OpenAI's ChatGPT-3.5, and ChatGPT-4, and Microsoft's Bing, in response to clinically relevant questions within the field of orthodontics. Ten open-type clinical orthodontics-related questions were posed to the LLMs. The responses provided by the LLMs were assessed on a scale ranging from 0 (minimum) to 10 (maximum) points, benchmarked against robust scientific evidence, including consensus statements and systematic reviews, using a predefined rubric. After a 4-week interval from the initial evaluation, the answers were reevaluated to gauge intra-evaluator reliability. Statistical comparisons were conducted on the scores using Friedman's and Wilcoxon's tests to identify the model providing the answers with the most comprehensiveness, scientific accuracy, clarity, and relevance. Overall, no statistically significant differences between the scores given by the two evaluators, on both scoring occasions, were detected, so an average score for every LLM was computed. The LLM answers scoring the highest, were those of Microsoft Bing Chat (average score = 7.1), followed by ChatGPT 4 (average score = 4.7), Google Bard (average score = 4.6), and finally ChatGPT 3.5 (average score 3.8). While Microsoft Bing Chat statistically outperformed ChatGPT-3.5 (P-value = 0.017) and Google Bard (P-value = 0.029), as well, and Chat GPT-4 outperformed Chat GPT-3.5 (P-value = 0.011), all models occasionally produced answers with a lack of comprehensiveness, scientific accuracy, clarity, and relevance. The questions asked were indicative and did not cover the entire field of orthodontics. Language models (LLMs) show great potential in supporting evidence-based orthodontics. However, their current limitations pose a potential risk of making incorrect healthcare decisions if utilized without careful consideration. Consequently, these tools cannot serve as a substitute for the orthodontist's essential critical thinking and comprehensive subject knowledge. For effective integration into practice, further research, clinical validation, and enhancements to the models are essential. Clinicians must be mindful of the limitations of LLMs, as their imprudent utilization could have adverse effects on patient care.

Makrygiannakis MA ，Giannakopoulos K ，Kaklamanos EG 《-》

Performance of artificial intelligence in bariatric surgery: comparative analysis of ChatGPT-4, Bing, and Bard in the American Society for Metabolic and Bariatric Surgery textbook of bariatric surgery questions.

The American Society for Metabolic and Bariatric Surgery (ASMBS) textbook serves as a comprehensive resource for bariatric surgery, covering recent advancements and clinical questions. Testing artificial intelligence (AI) engines using this authoritative source ensures accurate and up-to-date information and provides insight in its potential implications for surgical education and training. To determine the quality and to compare different large language models' (LLMs) ability to respond to textbook questions relating to bariatric surgery. Remote. Prompts to be entered into the LLMs were multiple-choice questions found in "The ASMBS Textbook of Bariatric Surgery, second Edition. The prompts were queried into 3 LLMs: OpenAI's ChatGPT-4, Microsoft's Bing, and Google's Bard. The generated responses were assessed based on overall accuracy, the number of correct answers according to subject matter, and the number of correct answers based on question type. Statistical analysis was performed to determine the number of responses per LLMs per category that were correct. Two hundred questions were used to query the AI models. There was an overall significant difference in the accuracy of answers, with an accuracy of 83.0% for ChatGPT-4, followed by Bard (76.0%) and Bing (65.0%). Subgroup analysis revealed a significant difference between the models' performance in question categories, with ChatGPT-4's demonstrating the highest proportion of correct answers in questions related to treatment and surgical procedures (83.1%) and complications (91.7%). There was also a significant difference between the performance in different question types, with ChatGPT-4 showing superior performance in inclusionary questions. Bard and Bing were unable to answer certain questions whereas ChatGPT-4 left no questions unanswered. LLMs, particularly ChatGPT-4, demonstrated promising accuracy when answering clinical questions related to bariatric surgery. Continued AI advancements and research is required to elucidate the potential applications of LLMs in training and education.

Lee Y ，Tessier L ，Brar K ，Malone S ，Jin D ，McKechnie T ，Jung JJ ，Kroh M ，Dang JT ，ASMBS Artificial Intelligence and Digital Surgery Taskforce ... -  《-》