ArticleFrontiers in oncology2026
Benchmarking large language models on a Chinese radiation oncology technology examination-preparation question set: accuracy, consensus, and efficiency for AI assisted education.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: This study evaluated GPT-4o, GPT-5.4, and two DeepSeek platform configurations using 1, 053 examination-preparation questions from a publicly and commercially available 2025 exercise collection for the Chinese National Radiation Oncology Technology Qualification Examination (Intermediate Level). We assessed accuracy rates, identical-response coverage, accuracy consensus, and model-reported latency estimates. Methods: Four configurations-GPT-4o, GPT-5.4, DS-Fast, and DS-Expert-were tested using an identical zero-shot Chinese prompt. Accuracy rates were summarized with Wilson 95% confidence intervals. Pairwise differences in the primary Chinese-language analysis were assessed using exact McNemar's tests with Holm adjustment. A descriptive language-controlled analysis evaluated English translations of the same questions using an equivalent English prompt. Results: Overall accuracy rates were 56.7% (95% CI, 53.7-59.7) for GPT-4o, 66.1% (63.2-68.9) for GPT-5.4, 98.4% (97.4-99.0) for DS-Fast, and 99.8% (99.3-99.9) for DS-Expert. All six overall pairwise comparisons remained significant after Holm adjustment. DS-Fast and DS-Expert produced identical responses for 1, 034 of 1, 053 questions (98.2%), all of which were correct. GPT-5.4 and DS-Expert showed 100% conditional accuracy among shared responses, but their identical-response coverage was only 65.9% (694/1, 053). Model-reported latency estimates were lowest for DS-Fast (0.68 ± 1.00 seconds) and highest for DS-Expert (3.69 ± 2.24 seconds). Under the English-translated condition, overall accuracy rates were 62.6%, 61.5%, 66.5%, and 76.9%, respectively. DS-Expert remained the most accurate configuration, although its advantage over the GPT models was reduced. Conclusion: DeepSeek configurations outperformed the GPT models on this Chinese-language examination-preparation question set, with DS-Expert achieving the highest accuracy. However, performance varied substantially by question language. These findings support further evaluation for answer verification and practice-question review, but they do not establish educational effectiveness.
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