Performance of ChatGPT on the Peruvian National Licensing Medical Examination: Cross-Sectional Study

GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination

PURPOSE

To assess the performance of GPT-4 Turbo with Vision (GPT-4TV), OpenAI's latest multimodal large language model, by comparing its ability to process both text and image inputs with that of the text-only GPT-4 Turbo (GPT-4 T) in the context of the Japan Diagnostic Radiology Board Examination (JDRBE).

MATERIALS AND METHODS

The dataset comprised questions from JDRBE 2021 and 2023. A total of six board-certified diagnostic radiologists discussed the questions and provided ground-truth answers by consulting relevant literature as necessary. The following questions were excluded: those lacking associated images, those with no unanimous agreement on answers, and those including images rejected by the OpenAI application programming interface. The inputs for GPT-4TV included both text and images, whereas those for GPT-4 T were entirely text. Both models were deployed on the dataset, and their performance was compared using McNemar's exact test. The radiological credibility of the responses was assessed by two diagnostic radiologists through the assignment of legitimacy scores on a five-point Likert scale. These scores were subsequently used to compare model performance using Wilcoxon's signed-rank test.

RESULTS

The dataset comprised 139 questions. GPT-4TV correctly answered 62 questions (45%), whereas GPT-4 T correctly answered 57 questions (41%). A statistical analysis found no significant performance difference between the two models (P = 0.44). The GPT-4TV responses received significantly lower legitimacy scores from both radiologists than the GPT-4 T responses.

CONCLUSION

No significant enhancement in accuracy was observed when using GPT-4TV with image input compared with that of using text-only GPT-4 T for JDRBE questions.

Evaluating the performance of ChatGPT-3.5 and ChatGPT-4 on the Taiwan plastic surgery board examination

Background

Chat Generative Pre-Trained Transformer (ChatGPT) is a state-of-the-art large language model that has been evaluated across various medical fields, with mixed performance on licensing examinations. This study aimed to assess the performance of ChatGPT-3.5 and ChatGPT-4 in answering questions from the Taiwan Plastic Surgery Board Examination.

Methods

The study evaluated the performance of ChatGPT-3.5 and ChatGPT-4 on 1375 questions from the past 8 years of the Taiwan Plastic Surgery Board Examination, including 985 single-choice and 390 multiple-choice questions. We obtained the responses between June and July 2023, launching a new chat session for each question to eliminate memory retention bias.

Results

Overall, ChatGPT-4 outperformed ChatGPT-3.5, achieving a 59 % correct answer rate compared to 41 % for ChatGPT-3.5. ChatGPT-4 passed five out of eight yearly exams, whereas ChatGPT-3.5 failed all. On single-choice questions, ChatGPT-4 scored 66 % correct, compared to 48 % for ChatGPT-3.5. On multiple-choice, ChatGPT-4 achieved a 43 % correct rate, nearly double the 23 % of ChatGPT-3.5.

Conclusion

As ChatGPT evolves, its performance on the Taiwan Plastic Surgery Board Examination is expected to improve further. The study suggests potential reforms, such as incorporating more problem-based scenarios, leveraging ChatGPT to refine exam questions, and integrating AI-assisted learning into candidate preparation. These advancements could enhance the assessment of candidates' critical thinking and problem-solving abilities in the field of plastic surgery.

Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis

BACKGROUND

Over the past 2 years, researchers have used various medical licensing examinations to test whether ChatGPT (OpenAI) possesses accurate medical knowledge. The performance of each version of ChatGPT on the medical licensing examination in multiple environments showed remarkable differences. At this stage, there is still a lack of a comprehensive understanding of the variability in ChatGPT's performance on different medical licensing examinations.

OBJECTIVE

In this study, we reviewed all studies on ChatGPT performance in medical licensing examinations up to March 2024. This review aims to contribute to the evolving discourse on artificial intelligence (AI) in medical education by providing a comprehensive analysis of the performance of ChatGPT in various environments. The insights gained from this systematic review will guide educators, policymakers, and technical experts to effectively and judiciously use AI in medical education.

METHODS

We searched the literature published between January 1, 2022, and March 29, 2024, by searching query strings in Web of Science, PubMed, and Scopus. Two authors screened the literature according to the inclusion and exclusion criteria, extracted data, and independently assessed the quality of the literature concerning Quality Assessment of Diagnostic Accuracy Studies-2. We conducted both qualitative and quantitative analyses.

RESULTS

A total of 45 studies on the performance of different versions of ChatGPT in medical licensing examinations were included in this study. GPT-4 achieved an overall accuracy rate of 81% (95% CI 78-84; P<.01), significantly surpassing the 58% (95% CI 53-63; P<.01) accuracy rate of GPT-3.5. GPT-4 passed the medical examinations in 26 of 29 cases, outperforming the average scores of medical students in 13 of 17 cases. Translating the examination questions into English improved GPT-3.5's performance but did not affect GPT-4. GPT-3.5 showed no difference in performance between examinations from English-speaking and non-English-speaking countries (P=.72), but GPT-4 performed better on examinations from English-speaking countries significantly (P=.02). Any type of prompt could significantly improve GPT-3.5's (P=.03) and GPT-4's (P<.01) performance. GPT-3.5 performed better on short-text questions than on long-text questions. The difficulty of the questions affected the performance of GPT-3.5 and GPT-4. In image-based multiple-choice questions (MCQs), ChatGPT's accuracy rate ranges from 13.1% to 100%. ChatGPT performed significantly worse on open-ended questions than on MCQs.

CONCLUSIONS

GPT-4 demonstrates considerable potential for future use in medical education. However, due to its insufficient accuracy, inconsistent performance, and the challenges posed by differing medical policies and knowledge across countries, GPT-4 is not yet suitable for use in medical education.

TRIAL REGISTRATION

PROSPERO CRD42024506687; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=506687.

Performance of Generative Artificial Intelligence in Dental Licensing Examinations

OBJECTIVES

Generative artificial intelligence (GenAI), including large language models (LLMs), has vast potential applications in health care and education. However, it is unclear how proficient LLMs are in interpreting written input and providing accurate answers in dentistry. This study aims to investigate the accuracy of GenAI in answering questions from dental licensing examinations.

METHODS

A total of 1461 multiple-choice questions from question books for the US and the UK dental licensing examinations were input into 2 versions of ChatGPT 3.5 and 4.0. The passing rates of the US and UK dental examinations were 75.0% and 50.0%, respectively. The performance of the 2 versions of GenAI in individual examinations and dental subjects was analysed and compared.

RESULTS

ChatGPT 3.5 correctly answered 68.3% (n = 509) and 43.3% (n = 296) of questions from the US and UK dental licensing examinations, respectively. The scores for ChatGPT 4.0 were 80.7% (n = 601) and 62.7% (n = 429), respectively. ChatGPT 4.0 passed both written dental licensing examinations, whilst ChatGPT 3.5 failed. ChatGPT 4.0 answered 327 more questions correctly and 102 incorrectly compared to ChatGPT 3.5 when comparing the 2 versions.

CONCLUSIONS

The newer version of GenAI has shown good proficiency in answering multiple-choice questions from dental licensing examinations. Whilst the more recent version of GenAI generally performed better, this observation may not hold true in all scenarios, and further improvements are necessary. The use of GenAI in dentistry will have significant implications for dentist-patient communication and the training of dental professionals.

Performance of Two Artificial Intelligence Generative Language Models on the Orthopaedic In-Training Examination

BACKGROUND

Artificial intelligence (AI) generative large language models are powerful and increasingly accessible tools with potential applications in health care education and training. The annual Orthopaedic In-Training Examination (OITE) is widely used to assess resident academic progress and preparation for the American Board of Orthopaedic Surgery Part 1 Examination.

MATERIALS AND METHODS

Open AI's ChatGPT and Google's Bard generative language models were administered the 2022 OITE. Question stems that contained images were input without and then with a text-based description of the imaging findings.

RESULTS

ChatGPT answered 69.1% of questions correctly. When provided with text describing accompanying media, this increased to 77.8% correct. In contrast, Bard answered 49.8% of questions correctly. This increased to 58% correct when text describing imaging in question stems was provided (P<.0001). ChatGPT was most accurate in questions within the shoulder category, with 90.9% correct. Bard performed best in the sports category, with 65.4% correct. ChatGPT performed above the published mean of Accreditation Council for Graduate Medical Education orthopedic resident test-takers (66%).

CONCLUSION

There is significant variability in the accuracy of publicly available AI models on the OITE. AI generative language software may play numerous potential roles in the future in orthopedic education, including simulating patient presentations and clinical scenarios, customizing individual learning plans, and driving evidence-based case discussion. Further research and collaboration within the orthopedic community is required to safely adopt these tools and minimize risks associated with their use. [Orthopedics. 2024;47(3):e146-e150.].

GPT-4/4V's performance on the Japanese National Medical Licensing Examination

BACKGROUND

Recent advances in Artificial Intelligence (AI) are changing the medical world, and AI will likely replace many of the actions performed by medical professionals. The overall clinical ability of the AI has been evaluated by its ability to answer a text-based national medical examination. This study uniquely assesses the performance of Open AI's ChatGPT against all Japanese National Medical Licensing Examination (NMLE), including images, illustrations, and pictures.

METHODS

We obtained the questions of the past six years of the NMLE (112th to 117th) from the Japanese Ministry of Health, Labour and Welfare website. We converted them to JavaScript Object Notation (JSON) format. We created an application programming interface (API) to output correct answers using GPT-4 for questions without images and GPT4-V(ision) or GPT4 console for questions with images.

RESULTS

The percentage of image questions was 723/2400 (30.1%) over the past six years. In all years, GPT-4/4V exceeded the minimum score the examinee should score. In total, over the six years, the percentage of correct answers for basic medical knowledge questions was 665/905 (73.5%); for clinical knowledge questions, 1143/1531 (74.7%); and for image questions 497/723 (68.7%), respectively.

CONCLUSIONS

Regarding medical knowledge, GPT-4/4V met the minimum criteria regardless of whether the questions included images, illustrations, and pictures. Our study sheds light on the potential utility of AI in medical education.

Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study

BACKGROUND

Previous research applying large language models (LLMs) to medicine was focused on text-based information. Recently, multimodal variants of LLMs acquired the capability of recognizing images.

OBJECTIVE

We aim to evaluate the image recognition capability of generative pretrained transformer (GPT)-4V, a recent multimodal LLM developed by OpenAI, in the medical field by testing how visual information affects its performance to answer questions in the 117th Japanese National Medical Licensing Examination.

METHODS

We focused on 108 questions that had 1 or more images as part of a question and presented GPT-4V with the same questions under two conditions: (1) with both the question text and associated images and (2) with the question text only. We then compared the difference in accuracy between the 2 conditions using the exact McNemar test.

RESULTS

Among the 108 questions with images, GPT-4V's accuracy was 68% (73/108) when presented with images and 72% (78/108) when presented without images (P=.36). For the 2 question categories, clinical and general, the accuracies with and those without images were 71% (70/98) versus 78% (76/98; P=.21) and 30% (3/10) versus 20% (2/10; P≥.99), respectively.

CONCLUSIONS

The additional information from the images did not significantly improve the performance of GPT-4V in the Japanese National Medical Licensing Examination.

ChatGPT (GPT-4) passed the Japanese National License Examination for Pharmacists in 2022, answering all items including those with diagrams: a descriptive study

PURPOSE

The objective of this study was to assess the performance of ChatGPT (GPT-4) on all items, including those with diagrams, in the Japanese National License Examination for Pharmacists (JNLEP) and compare it with the previous GPT-3.5 model’s performance.

METHODS

The 107th JNLEP, conducted in 2022, with 344 items input into the GPT-4 model, was targeted for this study. Separately, 284 items, excluding those with diagrams, were entered into the GPT-3.5 model. The answers were categorized and analyzed to determine accuracy rates based on categories, subjects, and presence or absence of diagrams. The accuracy rates were compared to the main passing criteria (overall accuracy rate ≥62.9%).

RESULTS

The overall accuracy rate for all items in the 107th JNLEP in GPT-4 was 72.5%, successfully meeting all the passing criteria. For the set of items without diagrams, the accuracy rate was 80.0%, which was significantly higher than that of the GPT-3.5 model (43.5%). The GPT-4 model demonstrated an accuracy rate of 36.1% for items that included diagrams.

CONCLUSION

Advancements that allow GPT-4 to process images have made it possible for LLMs to answer all items in medical-related license examinations. This study’s findings confirm that ChatGPT (GPT-4) possesses sufficient knowledge to meet the passing criteria.

A Preliminary Checklist (METRICS) to Standardize the Design and Reporting of Studies on Generative Artificial Intelligence-Based Models in Health Care Education and Practice: Development Study Involving a Literature Review

BACKGROUND

Adherence to evidence-based practice is indispensable in health care. Recently, the utility of generative artificial intelligence (AI) models in health care has been evaluated extensively. However, the lack of consensus guidelines on the design and reporting of findings of these studies poses a challenge for the interpretation and synthesis of evidence.

OBJECTIVE

This study aimed to develop a preliminary checklist to standardize the reporting of generative AI-based studies in health care education and practice.

METHODS

A literature review was conducted in Scopus, PubMed, and Google Scholar. Published records with "ChatGPT," "Bing," or "Bard" in the title were retrieved. Careful examination of the methodologies employed in the included records was conducted to identify the common pertinent themes and the possible gaps in reporting. A panel discussion was held to establish a unified and thorough checklist for the reporting of AI studies in health care. The finalized checklist was used to evaluate the included records by 2 independent raters. Cohen κ was used as the method to evaluate the interrater reliability.

RESULTS

The final data set that formed the basis for pertinent theme identification and analysis comprised a total of 34 records. The finalized checklist included 9 pertinent themes collectively referred to as METRICS (Model, Evaluation, Timing, Range/Randomization, Individual factors, Count, and Specificity of prompts and language). Their details are as follows: (1) Model used and its exact settings; (2) Evaluation approach for the generated content; (3) Timing of testing the model; (4) Transparency of the data source; (5) Range of tested topics; (6) Randomization of selecting the queries; (7) Individual factors in selecting the queries and interrater reliability; (8) Count of queries executed to test the model; and (9) Specificity of the prompts and language used. The overall mean METRICS score was 3.0 (SD 0.58). The tested METRICS score was acceptable, with the range of Cohen κ of 0.558 to 0.962 (P<.001 for the 9 tested items). With classification per item, the highest average METRICS score was recorded for the "Model" item, followed by the "Specificity" item, while the lowest scores were recorded for the "Randomization" item (classified as suboptimal) and "Individual factors" item (classified as satisfactory).

CONCLUSIONS

The METRICS checklist can facilitate the design of studies guiding researchers toward best practices in reporting results. The findings highlight the need for standardized reporting algorithms for generative AI-based studies in health care, considering the variability observed in methodologies and reporting. The proposed METRICS checklist could be a preliminary helpful base to establish a universally accepted approach to standardize the design and reporting of generative AI-based studies in health care, which is a swiftly evolving research topic.

Performance of ChatGPT on Stage 1 of the Taiwanese medical licensing exam

Introduction

Since its release by OpenAI in November 2022, numerous studies have subjected ChatGPT to various tests to evaluate its performance in medical exams. The objective of this study is to evaluate ChatGPT's accuracy and logical reasoning across all 10 subjects featured in Stage 1 of Senior Professional and Technical Examinations for Medical Doctors (SPTEMD) in Taiwan, with questions that encompass both Chinese and English.

Methods

In this study, we tested ChatGPT-4 to complete SPTEMD Stage 1. The model was presented with multiple-choice questions extracted from three separate tests conducted in February 2022, July 2022, and February 2023. These questions encompass 10 subjects, namely biochemistry and molecular biology, anatomy, embryology and developmental biology, histology, physiology, microbiology and immunology, parasitology, pharmacology, pathology, and public health. Subsequently, we analyzed the model's accuracy for each subject.

Result

In all three tests, ChatGPT achieved scores surpassing the 60% passing threshold, resulting in an overall average score of 87.8%. Notably, its best performance was in biochemistry, where it garnered an average score of 93.8%. Conversely, the performance of the generative pre-trained transformer (GPT)-4 assistant on anatomy, parasitology, and embryology was not as good. In addition, its scores were highly variable in embryology and parasitology.

Conclusion

ChatGPT has the potential to facilitate not only exam preparation but also improve the accessibility of medical education and support continuous education for medical professionals. In conclusion, this study has demonstrated ChatGPT's potential competence across various subjects within the SPTEMD Stage 1 and suggests that it could be a helpful tool for learning and exam preparation for medical students and professionals.

AI-Enabled Medical Education: Threads of Change, Promising Futures, and Risky Realities Across Four Potential Future Worlds

BACKGROUND

The rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues to transform various aspects of our lives, one critical question arises for medical education: what will be the nature of education, teaching, and learning in a future world where the acquisition, retention, and application of knowledge in the traditional sense are fundamentally altered by AI?

OBJECTIVE

The purpose of this perspective is to plan for the intersection of health care and medical education in the future.

METHODS

We used GPT-4 and scenario-based strategic planning techniques to craft 4 hypothetical future worlds influenced by AI's integration into health care and medical education. This method, used by organizations such as Shell and the Accreditation Council for Graduate Medical Education, assesses readiness for alternative futures and effectively manages uncertainty, risk, and opportunity. The detailed scenarios provide insights into potential environments the medical profession may face and lay the foundation for hypothesis generation and idea-building regarding responsible AI implementation.

RESULTS

The following 4 worlds were created using OpenAI's GPT model: AI Harmony, AI conflict, The world of Ecological Balance, and Existential Risk. Risks include disinformation and misinformation, loss of privacy, widening inequity, erosion of human autonomy, and ethical dilemmas. Benefits involve improved efficiency, personalized interventions, enhanced collaboration, early detection, and accelerated research.

CONCLUSIONS

To ensure responsible AI use, the authors suggest focusing on 3 key areas: developing a robust ethical framework, fostering interdisciplinary collaboration, and investing in education and training. A strong ethical framework emphasizes patient safety, privacy, and autonomy while promoting equity and inclusivity. Interdisciplinary collaboration encourages cooperation among various experts in developing and implementing AI technologies, ensuring that they address the complex needs and challenges in health care and medical education. Investing in education and training prepares professionals and trainees with necessary skills and knowledge to effectively use and critically evaluate AI technologies. The integration of AI in health care and medical education presents a critical juncture between transformative advancements and significant risks. By working together to address both immediate and long-term risks and consequences, we can ensure that AI integration leads to a more equitable, sustainable, and prosperous future for both health care and medical education. As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI's power while ensuring the safety and well-being of humanity.

Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students' and Physicians' Perceptions

BACKGROUND

ChatGPT is a well-known large language model-based chatbot. It could be used in the medical field in many aspects. However, some physicians are still unfamiliar with ChatGPT and are concerned about its benefits and risks.

OBJECTIVE

We aim to evaluate the perception of physicians and medical students toward using ChatGPT in the medical field.

METHODS

A web-based questionnaire was sent to medical students, interns, residents, and attending staff with questions regarding their perception toward using ChatGPT in clinical practice and medical education. Participants were also asked to rate their perception of ChatGPT's generated response about knee osteoarthritis.

RESULTS

Participants included 124 medical students, 46 interns, 37 residents, and 32 attending staff. After reading ChatGPT's response, 132 of the 239 (55.2%) participants had a positive rating about using ChatGPT for clinical practice. The proportion of positive answers was significantly lower in graduated physicians (48/115, 42%) compared with medical students (84/124, 68%; P<.001). Participants listed a lack of a patient-specific treatment plan, updated evidence, and a language barrier as ChatGPT's pitfalls. Regarding using ChatGPT for medical education, the proportion of positive responses was also significantly lower in graduate physicians (71/115, 62%) compared to medical students (103/124, 83.1%; P<.001). Participants were concerned that ChatGPT's response was too superficial, might lack scientific evidence, and might need expert verification.

CONCLUSIONS

Medical students generally had a positive perception of using ChatGPT for guiding treatment and medical education, whereas graduated doctors were more cautious in this regard. Nonetheless, both medical students and graduated doctors positively perceived using ChatGPT for creating patient educational materials.

Below average ChatGPT performance in medical microbiology exam compared to university students

Background

The transformative potential of artificial intelligence (AI) in higher education is evident, with conversational models like ChatGPT poised to reshape teaching and assessment methods. The rapid evolution of AI models requires a continuous evaluation. AI-based models can offer personalized learning experiences but raises accuracy concerns. MCQs are widely used for competency assessment. The aim of this study was to evaluate ChatGPT performance in medical microbiology MCQs compared to the students’ performance.

Methods

The study employed an 80-MCQ dataset from a 2021 medical microbiology exam at the University of Jordan Doctor of Dental Surgery (DDS) Medical Microbiology 2 course. The exam contained 40 midterm and 40 final MCQs, authored by a single instructor without copyright issues. The MCQs were categorized based on the revised Bloom’s Taxonomy into four categories: Remember, Understand, Analyze, or Evaluate. Metrics, including facility index and discriminative efficiency, were derived from 153 midterm and 154 final exam DDS student performances. ChatGPT 3.5 was used to answer questions, and responses were assessed for correctness and clarity by two independent raters.

Results

ChatGPT 3.5 correctly answered 64 out of 80 medical microbiology MCQs (80%) but scored below the student average (80.5/100 vs. 86.21/100). Incorrect ChatGPT responses were more common in MCQs with longer choices (p = 0.025). ChatGPT 3.5 performance varied across cognitive domains: Remember (88.5% correct), Understand (82.4% correct), Analyze (75% correct), Evaluate (72% correct), with no statistically significant differences (p = 0.492). Correct ChatGPT responses received statistically significant higher average clarity and correctness scores compared to incorrect responses.

Conclusion

The study findings emphasized the need for ongoing refinement and evaluation of ChatGPT performance. ChatGPT 3.5 showed the potential to correctly and clearly answer medical microbiology MCQs; nevertheless, its performance was below-bar compared to the students. Variability in ChatGPT performance in different cognitive domains should be considered in future studies. The study insights could contribute to the ongoing evaluation of the AI-based models’ role in educational assessment and to augment the traditional methods in higher education.

Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study

PURPOSE

We aimed to describe the performance and evaluate the educational value of justifications provided by artificial intelligence chatbots, including GPT-3.5, GPT-4, Bard, Claude, and Bing, on the Peruvian National Medical Licensing Examination (P-NLME).

METHODS

This was a cross-sectional analytical study. On July 25, 2023, each multiple-choice question (MCQ) from the P-NLME was entered into each chatbot (GPT-3, GPT-4, Bing, Bard, and Claude) 3 times. Then, 4 medical educators categorized the MCQs in terms of medical area, item type, and whether the MCQ required Peru-specific knowledge. They assessed the educational value of the justifications from the 2 top performers (GPT-4 and Bing).

RESULTS

GPT-4 scored 86.7% and Bing scored 82.2%, followed by Bard and Claude, and the historical performance of Peruvian examinees was 55%. Among the factors associated with correct answers, only MCQs that required Peru-specific knowledge had lower odds (odds ratio, 0.23; 95% confidence interval, 0.09-0.61), whereas the remaining factors showed no associations. In assessing the educational value of justifications provided by GPT-4 and Bing, neither showed any significant differences in certainty, usefulness, or potential use in the classroom.

CONCLUSION

Among chatbots, GPT-4 and Bing were the top performers, with Bing performing better at Peru-specific MCQs. Moreover, the educational value of justifications provided by the GPT-4 and Bing could be deemed appropriate. However, it is essential to start addressing the educational value of these chatbots, rather than merely their performance on examinations.