Evaluating Bard Gemini Pro and GPT-4 Vision Against Student Performance in Medical Visual Question Answering: Comparative Case Study
Bibliographic record
Abstract
Background: The rapid development of large language models (LLMs) such as OpenAI's ChatGPT has significantly impacted medical research and education. These models have shown potential in fields ranging from radiological imaging interpretation to medical licensing examination assistance. Recently, LLMs have been enhanced with image recognition capabilities. Objective: This study aims to critically examine the effectiveness of these LLMs in medical diagnostics and training by assessing their accuracy and utility in answering image-based questions from medical licensing examinations. Methods: This study analyzed 1070 image-based multiple-choice questions from the AMBOSS learning platform, divided into 605 in English and 465 in German. Customized prompts in both languages directed the models to interpret medical images and provide the most likely diagnosis. Student performance data were obtained from AMBOSS, including metrics such as the "student passed mean" and "majority vote." Statistical analysis was conducted using Python (Python Software Foundation), with key libraries for data manipulation and visualization. Results: GPT-4 1106 Vision Preview (OpenAI) outperformed Bard Gemini Pro (Google), correctly answering 56.9% (609/1070) of questions compared to Bard's 44.6% (477/1070), a statistically significant difference (χ2₁=32.1, P<.001). However, GPT-4 1106 left 16.1% (172/1070) of questions unanswered, significantly higher than Bard's 4.1% (44/1070; χ2₁=83.1, P<.001). When considering only answered questions, GPT-4 1106's accuracy increased to 67.8% (609/898), surpassing both Bard (477/1026, 46.5%; χ2₁=87.7, P<.001) and the student passed mean of 63% (674/1070, SE 1.48%; χ2₁=4.8, P=.03). Language-specific analysis revealed both models performed better in German than English, with GPT-4 1106 showing greater accuracy in German (282/465, 60.65% vs 327/605, 54.1%; χ2₁=4.4, P=.04) and Bard Gemini Pro exhibiting a similar trend (255/465, 54.8% vs 222/605, 36.7%; χ2₁=34.3, P<.001). The student majority vote achieved an overall accuracy of 94.5% (1011/1070), significantly outperforming both artificial intelligence models (GPT-4 1106: χ2₁=408.5, P<.001; Bard Gemini Pro: χ2₁=626.6, P<.001). Conclusions: Our study shows that GPT-4 1106 Vision Preview and Bard Gemini Pro have potential in medical visual question-answering tasks and to serve as a support for students. However, their performance varies depending on the language used, with a preference for German. They also have limitations in responding to non-English content. The accuracy rates, particularly when compared to student responses, highlight the potential of these models in medical education, yet the need for further optimization and understanding of their limitations in diverse linguistic contexts remains critical.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".