Performance of DeepSeek-R1 in ophthalmology: an evaluation of clinical decision-making and cost-effectiveness
Bibliographic record
Abstract
BACKGROUND/AIMS: To compare the performance and cost-effectiveness of DeepSeek-R1 with OpenAI o1 in diagnosing and managing ophthalmology clinical cases. METHODS: In this cross-sectional study, a total of 300 clinical cases spanning 10 ophthalmology subspecialties were collected from StatPearls, each with a multiple-choice question on diagnosis or management. DeepSeek-R1 was accessed through its public chat interface, while OpenAI o1 was queried via its Application Programming Interface with a standardised temperature of 0.3. Both models were prompted using plan-and-solve+. Performance was calculated as the proportion of correct answers. McNemar's test was employed to compare the two models' performance on paired data. Intermodel agreement for correct diagnoses was evaluated via Cohen's kappa. Token-based cost analyses were performed to estimate the comparative expenditures of running each model at scale, including input prompts and model-generated output. RESULTS: DeepSeek-R1 and OpenAI o1 achieved an identical overall performance of 82.0% (n=246/300; 95% CI: 77.3 to 85.9). Subspecialty-specific analysis revealed numerical variation in performance, though none of these comparisons reached statistical significance (p>0.05). Agreement in performance between the models was moderate overall (κ=0.503, p<0.001), with substantial agreement in refractive management/intervention (κ=0.698, p<0.001) and moderate agreement in retina/vitreous (κ=0.561, p<0.001) and ocular pathology/oncology (κ=0.495, p<0.01) cases. Cost analysis indicated an approximately 15-fold reduction in per-query, token-related expenses when using DeepSeek-R1 vs OpenAI o1 for the same workload. CONCLUSIONS: DeepSeek-R1 shows strong diagnostic and management performance comparable to OpenAI o1 across ophthalmic subspecialties, while significantly reducing costs. These results support its use as a cost-effective, open-weight alternative to proprietary models.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".