DeepSeek-R1 vs OpenAI o1 for Ophthalmic Diagnoses and Management Plans
Bibliographic record
Abstract
Importance: Large language models (LLMs) are increasingly being explored in clinical decision-making, but few studies have evaluated their performance on complex ophthalmology cases from clinical practice settings. Understanding whether open-weight, reasoning-enhanced LLMs can outperform proprietary models has implications for clinical utility and accessibility. Objective: To evaluate the diagnostic accuracy, management decision-making, and cost of DeepSeek-R1 vs OpenAI o1 across diverse ophthalmic subspecialties. Design, Setting, and Participants: This was a cross-sectional evaluation conducted using standardized prompts and model configurations. Clinical cases were sourced from JAMA Ophthalmology's Clinical Challenge articles, containing complex cases from clinical practice settings. Each case included an open-ended diagnostic question and a multiple-choice next-step decision. All cases were included without exclusions, and no human participants were involved. Data were analyzed from March 13 to March 30, 2025. Exposures: DeepSeek-R1and OpenAI o1 were evaluated using the Plan-and-Solve Plus (PS+) prompt engineering method. Main Outcomes and Measures: Primary outcomes were diagnostic accuracy and next-step decision-making accuracy, defined as the proportion of correct responses. Token cost analyses were performed to estimate expenses. Intermodel agreement was evaluated using Cohen κ, and McNemar test was used to compare performance. Results: A total of 422 clinical cases were included, spanning 10 subspecialties. DeepSeek-R1 achieved a higher diagnostic accuracy of 70.4% (297 of 422 cases) compared with 63.0% (266 of 422 cases) for OpenAI o1, a 7.3% difference (95% CI, 1.0%-13.7%; P = .02). For next-step decisions, DeepSeek-R1 was correct in 82.7% of cases (349 of 422 cases) vs OpenAI o1's accuracy of 75.8% (320 of 422 cases), a 6.9% difference (95% CI, 1.4%-12.3%; P = .01). Intermodel agreement was moderate (κ = 0.422; 95% CI, 0.375-0.469; P < .001). DeepSeek-R1 offered lower costs per query than OpenAI o1, with savings exceeding 66-fold (up to 98.5%) during off-peak pricing. Conclusions and Relevance: DeepSeek-R1 outperformed OpenAI o1 in diagnosis and management across subspecialties while lowering operating costs, supporting the potential of open-weight, reinforcement learning-augmented LLMs as scalable and cost-saving tools for clinical decision support. Further investigations should evaluate safety guardrails and assess performance of self-hosted adaptations of DeepSeek-R1 with domain-specific ophthalmic expertise to optimize clinical utility.
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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 | 0.035 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".