Assessment of Resident and AI Chatbot Performance on the University of Toronto Family Medicine Residency Progress Test: Comparative Study.
Large language model (LLM)-based chatbots are evolving at an unprecedented pace with the release of ChatGPT, specifically GPT-3.5, and its successor, GPT-4. Their capabilities in general-purpose tasks and language generation have advanced to the point of performing excellently on various educational examination benchmarks, including medical knowledge tests. Comparing the performance of these 2 LLM models to that of Family Medicine residents on a multiple-choice medical knowledge test can provide insights into their potential as medical education tools.
This study aimed to quantitatively and qualitatively compare the performance of GPT-3.5, GPT-4, and Family Medicine residents in a multiple-choice medical knowledge test appropriate for the level of a Family Medicine resident.
An official University of Toronto Department of Family and Community Medicine Progress Test consisting of multiple-choice questions was inputted into GPT-3.5 and GPT-4. The artificial intelligence chatbot's responses were manually reviewed to determine the selected answer, response length, response time, provision of a rationale for the outputted response, and the root cause of all incorrect responses (classified into arithmetic, logical, and information errors). The performance of the artificial intelligence chatbots were compared against a cohort of Family Medicine residents who concurrently attempted the test.
GPT-4 performed significantly better compared to GPT-3.5 (difference 25.0%, 95% CI 16.3%-32.8%; McNemar test: P<.001); it correctly answered 89/108 (82.4%) questions, while GPT-3.5 answered 62/108 (57.4%) questions correctly. Further, GPT-4 scored higher across all 11 categories of Family Medicine knowledge. In 86.1% (n=93) of the responses, GPT-4 provided a rationale for why other multiple-choice options were not chosen compared to the 16.7% (n=18) achieved by GPT-3.5. Qualitatively, for both GPT-3.5 and GPT-4 responses, logical errors were the most common, while arithmetic errors were the least common. The average performance of Family Medicine residents was 56.9% (95% CI 56.2%-57.6%). The performance of GPT-3.5 was similar to that of the average Family Medicine resident (P=.16), while the performance of GPT-4 exceeded that of the top-performing Family Medicine resident (P<.001).
GPT-4 significantly outperforms both GPT-3.5 and Family Medicine residents on a multiple-choice medical knowledge test designed for Family Medicine residents. GPT-4 provides a logical rationale for its response choice, ruling out other answer choices efficiently and with concise justification. Its high degree of accuracy and advanced reasoning capabilities facilitate its potential applications in medical education, including the creation of exam questions and scenarios as well as serving as a resource for medical knowledge or information on community services.
Huang RS
，Lu KJQ
，Meaney C
，Kemppainen J
，Punnett A
，Leung FH
... -
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Large Language Models Take on Cardiothoracic Surgery: A Comparative Analysis of the Performance of Four Models on American Board of Thoracic Surgery Exam Questions in 2023.
Objectives Large language models (LLMs), for example, ChatGPT, have performed exceptionally well in various fields. Of note, their success in answering postgraduate medical examination questions has been previously reported, indicating their possible utility in surgical education and training. This study evaluated the performance of four different LLMs on the American Board of Thoracic Surgery's (ABTS) Self-Education and Self-Assessment in Thoracic Surgery (SESATS) XIII question bank to investigate the potential applications of these LLMs in the education and training of future surgeons. Methods The dataset in this study comprised 400 best-of-four questions from the SESATS XIII exam. This included 220 adult cardiac surgery questions, 140 general thoracic surgery questions, 20 congenital cardiac surgery questions, and 20 cardiothoracic critical care questions. The GPT-3.5 (OpenAI, San Francisco, CA) and GPT-4 (OpenAI) models were evaluated, as well as Med-PaLM 2 (Google Inc., Mountain View, CA) and Claude 2 (Anthropic Inc., San Francisco, CA), and their respective performances were compared. The subspecialties included were adult cardiac, general thoracic, congenital cardiac, and critical care. Questions requiring visual information, such as clinical images or radiology, were excluded. Results GPT-4 demonstrated a significant improvement over GPT-3.5 overall (87.0% vs. 51.8% of questions answered correctly, p < 0.0001). GPT-4 also exhibited consistently improved performance across all subspecialties, with accuracy rates ranging from 70.0% to 90.0%, compared to 35.0% to 60.0% for GPT-3.5. When using the GPT-4 model, ChatGPT performed significantly better on the adult cardiac and general thoracic subspecialties (p < 0.0001). Conclusions Large language models, such as ChatGPT with the GPT-4 model, demonstrate impressive skill in understanding complex cardiothoracic surgical clinical information, achieving an overall accuracy rate of nearly 90.0% on the SESATS question bank. Our study shows significant improvement between successive GPT iterations. As LLM technology continues to evolve, its potential use in surgical education, training, and continuous medical education is anticipated to enhance patient outcomes and safety in the future.
Khalpey Z
，Kumar U
，King N
，Abraham A
，Khalpey AH
... -
《Cureus》
ResearchGate
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钛学术
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