DeepSeek-R1 vs OpenAI o1 for Ophthalmic Diagnoses and Management Plans
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».