Performance of an Artificial Intelligence Chatbot in Ophthalmic Knowledge Assessment
Bibliographic record
Abstract
Importance: ChatGPT is an artificial intelligence (AI) chatbot that has significant societal implications. Training curricula using AI are being developed in medicine, and the performance of chatbots in ophthalmology has not been characterized. Objective: To assess the performance of ChatGPT in answering practice questions for board certification in ophthalmology. Design, Setting, and Participants: This cross-sectional study used a consecutive sample of text-based multiple-choice questions provided by the OphthoQuestions practice question bank for board certification examination preparation. Of 166 available multiple-choice questions, 125 (75%) were text-based. Exposures: ChatGPT answered questions from January 9 to 16, 2023, and on February 17, 2023. Main Outcomes and Measures: Our primary outcome was the number of board certification examination practice questions that ChatGPT answered correctly. Our secondary outcomes were the proportion of questions for which ChatGPT provided additional explanations, the mean length of questions and responses provided by ChatGPT, the performance of ChatGPT in answering questions without multiple-choice options, and changes in performance over time. Results: In January 2023, ChatGPT correctly answered 58 of 125 questions (46%). ChatGPT's performance was the best in the category general medicine (11/14; 79%) and poorest in retina and vitreous (0%). The proportion of questions for which ChatGPT provided additional explanations was similar between questions answered correctly and incorrectly (difference, 5.82%; 95% CI, -11.0% to 22.0%; χ21 = 0.45; P = .51). The mean length of questions was similar between questions answered correctly and incorrectly (difference, 21.4 characters; SE, 36.8; 95% CI, -51.4 to 94.3; t = 0.58; df = 123; P = .22). The mean length of responses was similar between questions answered correctly and incorrectly (difference, -80.0 characters; SE, 65.4; 95% CI, -209.5 to 49.5; t = -1.22; df = 123; P = .22). ChatGPT selected the same multiple-choice response as the most common answer provided by ophthalmology trainees on OphthoQuestions 44% of the time. In February 2023, ChatGPT provided a correct response to 73 of 125 multiple-choice questions (58%) and 42 of 78 stand-alone questions (54%) without multiple-choice options. Conclusions and Relevance: ChatGPT answered approximately half of questions correctly in the OphthoQuestions free trial for ophthalmic board certification preparation. Medical professionals and trainees should appreciate the advances of AI in medicine while acknowledging that ChatGPT as used in this investigation did not answer sufficient multiple-choice questions correctly for it to provide substantial assistance in preparing for board certification at this time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".