Abstract
Introduction: This study aims to evaluate the performance of ChatGPT-4, an advanced artificial intelligence (AI) language model, on the Ophthalmology Knowledge Assessment Program (OKAP) examination compared to its predecessor, ChatGPT-3.5.
Methods: Both models were tested on 180 OKAP practice questions covering various ophthalmology subject categories.
Results: ChatGPT-4 significantly outperformed ChatGPT-3.5 (81% vs. 57%; p <0.001), indicating improvements in medical knowledge assessment.
Discussion: The superior performance of ChatGPT-4 suggests potential applicability in ophthalmologic education and clinical decision support systems. Future research should focus on refining AI models, ensuring a balanced representation of fundamental and specialized knowledge, and determining the optimal method of integrating AI into medical education and practice.
Keywords: artificial intelligence, Ophthalmology Knowledge Assessment Program, OKAP, ChatGPT, medical education
The rapid development of artificial intelligence (AI) and natural language processing has opened new possibilities in various domains, including health care, education, and research. The application of these foundation models in medicine has been an area of interest, with attempts to have machines take medical qualifying examinations. For example, in 2017, news reports described a Chinese AI model called Xiaoyi, which was trained on 2 million medical records and 400,000 articles. Reports claimed that Xiaoyi was able to score well above the human mean (360) on the Chinese medical licensing examination with a score of 456. 1 More recently, an AI model passed two sets of the UK Royal College of Radiology examination with an overall accuracy of 79.5% compared with 26 radiologists who passed with 84.8% accuracy. 2 The PaLM large language model was recently tested on the United States Medical Licensing Examination and other medical question-answering challenges, including consumer health questions. The results showed a significant improvement over previous AI models, with the PaLM model achieving 67.6% accuracy. 3 OpenAI's GPT (Generative Pre-trained Transformer) series consistently demonstrates improved language understanding and knowledge representation with each successive iteration. The latest version, ChatGPT-4, has been reported to have superior performance compared with its predecessors. 4 This study aims to evaluate the performance of ChatGPT-4 on the Ophthalmology Knowledge Assessment Program (OKAP) examination compared with ChatGPT-3.5 to determine the potential applicability of this AI model in medical education and clinical practice.
Methods
The performance of ChatGPT-4 was compared with ChatGPT-3.5 on OKAP practice questions published by the American Academy of Ophthalmology (AAO) under the Basic Clinic and Science Course (BCSC) to evaluate the effectiveness of popular language models in ophthalmologic knowledge. 5 The OKAP examination is an annual, multiple-choice examination administered to ophthalmology residents in the United States, designed to assess their knowledge in various ophthalmology subspecialties. The BCSC, sponsored by the AAO, contains a series of OKAP practice questions designed to help resident physicians prepare for the examination.
ChatGPT-3.5 and ChatGPT-4 were provided with the same 180 questions from the BCSC question bank. These questions covered the following ophthalmologic subcategories, as defined by the AAO: cornea, neurology, retina, optics, glaucoma, cataract, oculoplastics, fundamentals, pathology, pediatrics, refractive surgery, and uveitis. ChatGPT-3.5 was queried on December 28 and 29, 2022, and ChatGPT-4 was queried on March 15 and 16, 2023. Questions with images in the prompt were removed from the analysis because at the time of querying, ChatGPT could not process images. This resulted in 167 questions being analyzed. Each model was instructed to “select the best answer option and explain why this option was chosen,” followed by each question. If the algorithm did not select an answer option, a second request was used, “please select the best answer option and explain why that option was selected.” The percentage of questions correctly answered was then evaluated according to the answer key provided.
Statistical analysis was performed using SPSS Statistics Software (version 21, SPSS Inc., Chicago, IL). A comparison between the performance of both versions was performed using the chi-square test. A p < 0.05 was considered statistically significant.
Results
ChatGPT-4 performed significantly better than ChatGPT-3.5 (81 vs. 57%; p < 0.001) on the 167 OKAP sample questions answered by both models. When comparing each category individually, the performance of ChatGPT-4 was superior to that of ChatGPT-3.5 for all categories other than: “fundamentals” ( Fig. 1 ); however, there was not a significant difference due to the small number of questions from each section ( Table 1 ).
Fig. 1.
Comparing the performance of ChatGPT-3.5 with ChatGPT-4 on each category of questions from the Ophthalmology Knowledge Assessment Program (OKAP) examination.
Table 1. ChatGPT-3.5 versus ChatGPT-4: total questions, correct and incorrect by category.
| ChatGPT-3.5 | ChatGPT-4 | Total questions | |||
|---|---|---|---|---|---|
| Correct answers | Incorrect answers | Correct answers | Incorrect answers | ||
| Cornea | 8 | 4 | 12 | 0 | 12 |
| Neuro | 4 | 9 | 13 | 0 | 13 |
| Retina | 9 | 4 | 11 | 2 | 13 |
| Optics | 7 | 8 | 11 | 4 | 15 |
| Glaucoma | 3 | 8 | 8 | 3 | 11 |
| Cataracts | 9 | 6 | 11 | 4 | 15 |
| Plastics | 9 | 5 | 10 | 4 | 14 |
| Fundamentals | 10 | 5 | 10 | 5 | 15 |
| Pathology | 9 | 6 | 13 | 2 | 15 |
| Pediatrics | 8 | 6 | 10 | 4 | 14 |
| Refractive surgery | 8 | 7 | 13 | 2 | 15 |
| Uveitis | 11 | 4 | 14 | 1 | 15 |
| Total | 95 | 72 | 136 | 31 | 167 |
Note: Frequency of correct and incorrect answers by category of Ophthalmology Knowledge Assessment Program question.
Discussion
ChatGPT-4 scored significantly higher on the OKAP examination than ChatGPT-3.5. This finding supports the hypothesis that the enhancements made in the ChatGPT-4 model, including architecture improvements, expanded training dataset, which included a more diverse and up-to-date dataset, as well as refined fine-tuning processes, contribute to its superior performance in medical knowledge assessment. 4 The superior performance of ChatGPT-4 has several implications for medical education and AI application in the health care sector.
Primarily, ChatGPT-4 provides ophthalmologists with rapid access to a vast amount of medical knowledge that will continue to update and presumably improve with each new version. With a score of 80% correct, ChatGPT-4 scored slightly above the average performance of humans on BCSC questions. 6 The performance of ChatGPT-4 relative to ChatGPT-3.5 and human test takers demonstrates that the newest version of ChatGPT has the knowledge network capable of providing valuable impacts on clinical practice and medical education. It is reasonable to suggest that ChatGPT-4's improved understanding of medical concepts and reasoning could be leveraged in clinical decision support systems, providing residents with relevant information quickly to aid their decision-making processes. However, the need for improvement in fundamental knowledge questions is necessary because when ChatGPT answers a question incorrectly, it generates text indicating why another answer is correct even though that is not the correct answer. This could be detrimental to learning and could negatively affect both residents and patients if applied to a clinical setting.
Our theory is that ChatGPT-4 did not improve in its performance on questions pertaining to fundamental ophthalmology knowledge because fundamental knowledge represents essential and established information, which inherently would not be as frequently updated in recent literature and databases compared with highly nuanced or specialized topics. Consequently, the model may not have frequently encountered novel data about these fundamental concepts during its updated training. To address this issue, it is crucial to ensure a balanced and comprehensive representation of fundamental and specialized ophthalmology knowledge in the training dataset and to invest in refining the model's understanding of abstract and general concepts.
Nevertheless, it is essential to recognize the limitations of our study. The models were assessed using multiple-choice questions, which may not fully capture the intricacies of real-world clinical situations. Despite these limitations, the study provides valuable insights into the potential use of AI models in medical education and health care. The significant improvement of ChatGPT-4 over ChatGPT-3.5 in the OKAP examination serves as an indicator of the rapid advancement of AI capabilities in the medical domain. However, it is crucial to approach the integration of AI into medical practice with caution, as ethical considerations, potential biases, and the importance of human interaction in patient care must be thoroughly considered.
ChatGPT could be used to complement traditional learning methods and not as a replacement for human instruction, mentorship, or care delivery. Integrating AI models in medical education and practice have the risk of potential biases, unknown ethical approaches, and the loss of human interaction in patient care. 7 8 Future research should focus on the applicability of ChatGPT-4 with particular attention focusing on the slower response rate of more advanced ChatGPT models. 4 Additionally, further investigation should be conducted to determine the optimal method of integrating AI models into ophthalmology education and clinical practice, ensuring that these tools are used effectively and ethically.
Conclusion
In conclusion, our study reveals that ChatGPT-4 significantly outperforms ChatGPT-3.5 on the OKAP examination, indicating the potential for enhanced AI models to support medical education and practice. As AI continues to advance, the medical education community needs to remain engaged with these developments, ensuring that the potential benefits of AI are maximized while minimizing the risks associated with its implementation in the health care sector.
Funding Statement
Funding/Acknowledgment No financial support was received in pursuant to this research.
Footnotes
Conflict of Interest None declared.
References
- 1.Yan A.How a robot passed China's medical licensing examscmp.com. Published November 20,2017. Accessed January 02, 2023 at:https://www.scmp.com/news/china/society/article/2120724/how-robot-passed-chinas-medical-licensing-exam
- 2.Shelmerdine S C, Martin H, Shirodkar K, Shamshuddin S, Weir-McCall J R. Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study. BMJ. 2022;x:e072826. doi: 10.1136/bmj-2022-072826. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Singhal K, Azizi S, Tu Tet al. Large Language Models Encode Clinical KnowledgePublished online December 26, 2022. Accessed March 30, 2023 at:http://arxiv.org/abs/2212.13138
- 4.GPT-4 is OpenAI's most advanced system, producing safer and more useful responsesOpenAI; Accessed August 29, 2023 at:https://openai.com/product/gpt-4
- 5.American Academy of Ophthalmology Basic and Clinical Science Course Self-Assessment ProgramAccessed December 03, 2023 at:https://store.aao.org/basic-and-clinical-science-course-self-assessment-program.html
- 6.Antaki F, Touma S, Milad D, El-Khoury J, Duval R. Evaluating the performance of ChatGPT in ophthalmology: an analysis of its successes and shortcomings. Ophthalmol Sci. 2023;3(04):100324. doi: 10.1016/j.xops.2023.100324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wiens J, Saria S, Sendak M et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(09):1337–1340. doi: 10.1038/s41591-019-0548-6. [DOI] [PubMed] [Google Scholar]
- 8.Oke I. The pursuit of generalizability and equity through artificial intelligence-based risk prediction models. JAMA Ophthalmol. 2022;140(08):798–799. doi: 10.1001/jamaophthalmol.2022.2139. [DOI] [PubMed] [Google Scholar]