Chinese generative AI models (DeepSeek and Qwen) rival ChatGPT-4 in ophthalmology queries with excellent performance in Arabic and English

Abstract

The rapid evolution of generative artificial intelligence (genAI) has ushered in a new era of digital medical consultations, with patients turning to AI-driven tools for guidance. The emergence of Chinese-developed genAI models such as DeepSeek-R1 and Qwen-2.5 presented a challenge to the dominance of OpenAI's ChatGPT. The aim of this study was to benchmark the performance of Chinese genAI models against ChatGPT-40 and to assess disparities in performance across English and Arabic. Following the METRICS checklist for genAI evaluation, Qwen-2.5, DeepSeek-R1, and ChatGPT-40 were assessed for completeness, accuracy, and relevance using the CLEAR tool in common patient ophthalmology queries. In English, Qwen-2.5 demonstrated the highest overall performance (CLEAR score: 4.43 ± 0.28), outperforming both DeepSeek-R1 (4.3 ± 0.43) and ChatGPT-40 (4.14 ± 0.41), with p = 0.002. A similar hierarchy emerged in Arabic, with Qwen-2.5 again leading (4.40 ± 0.29), followed by DeepSeek-R1 (4.20 ± 0.49) and ChatGPT-40 (4.14 ± 0.41), with p = 0.007. Each tested genAI model exhibited near-identical performance across the two languages, with ChatGPT-40 demonstrating the most balanced linguistic capabilities (p = 0.957), while Qwen-2.5 and DeepSeek-R1 showed a marginal superiority for English. An in-depth examination of genAI performance across key CLEAR components revealed that Qwen-2.5 consistently excelled in content completeness, factual accuracy, and relevance in both English and Arabic, setting a new benchmark for genAI in medical inquiries. Despite minor linguistic disparities, all three models exhibited robust multilingual capabilities, challenging the long-held assumption that genAI is inherently biased toward English. These findings highlight the evolving nature of AI-driven medical assistance, with Chinese genAI models being able to rival or even surpass ChatGPT-40 in ophthalmology-related queries.

Keywords: DeepSeek; LLM; OpenAI; Qwen; eye disease.

Figure 1.

Comparison of generative AI (genAI) model performance in English and Arabic using CLEAR overall scores. The p-values were calculated using Kruskal-Wallis test.

Figure 2.

Comparison of generative AI (genAI) model performance across ophthalmology query topics. p-values were calculated using Kruskal-Wallis test. Post-hoc analysis results using Mann- Whitney U tests are indicated by the horizontal lines between genAI models, with significant results indicated by asterisk, while statistically insignificant results are indicated by ns.