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Background <p>Large language models (LLMs), such as OpenAI o1 and DeepSeek-R1, demonstrate promising applications in healthcare through structured reasoning and decision support. This study evaluates the responses and chain-of-thought (CoT) outputs of OpenAI o1and DeepSeek-R1 in answering questions about colorectal cancer (CRC) screening.</p> Methods <p>Fifteen questions about CRC screening were posed to OpenAI o1 and DeepSeek-R1. Four experts rated the responses for accuracy and comprehensiveness and three further experts evaluated the CoT reasoning output for logical-coherence and error-types and handling, using the National Comprehensive Cancer Network (NCCN) guidelines as the primary reference standard.</p> Results <p>Both LLMs demonstrated high accuracy without significant differences (median accuracy scores: OpenAI o1 = 4.5, DeepSeek-R1 = 5; <i>p</i> = 0.5243). However, DeepSeek-R1 significantly outperformed OpenAI o1 in comprehensiveness (<i>p</i> &lt; 0.0001), logical coherence (<i>p</i> = 0.0001), and error types and handling (<i>p</i> = 0.0149). DeepSeek-R1 generated more detailed responses (word count: 110 ± 40 vs. 57 ± 24, <i>p</i> = 0.0001), with longer response times (25 ± 10s vs. 7 ± 4s, <i>p</i> &lt; 0.0001).</p> Conclusion <p>DeepSeek-R1 and OpenAI o1 both offer high accuracy for CRC screening guidance, with DeepSeek-R1 providing more comprehensive responses with logically more coherent, and robust error-handling reasoning process, compared with OpenAI o1. Context-specific evaluation is critical for practical clinical integration.</p>

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DeepSeek-R1 vs. OpenAI o1 in colorectal cancer screening: a binational evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Yongpeng Chen,
  • Sen Deng,
  • Priska Hakenberg,
  • Chengpeng Li,
  • Tianzuo Zhan,
  • Dejun Fan,
  • XuTao Lin,
  • Xianzhe Li,
  • Cui Yang,
  • Jiancong Hu

摘要

Background

Large language models (LLMs), such as OpenAI o1 and DeepSeek-R1, demonstrate promising applications in healthcare through structured reasoning and decision support. This study evaluates the responses and chain-of-thought (CoT) outputs of OpenAI o1and DeepSeek-R1 in answering questions about colorectal cancer (CRC) screening.

Methods

Fifteen questions about CRC screening were posed to OpenAI o1 and DeepSeek-R1. Four experts rated the responses for accuracy and comprehensiveness and three further experts evaluated the CoT reasoning output for logical-coherence and error-types and handling, using the National Comprehensive Cancer Network (NCCN) guidelines as the primary reference standard.

Results

Both LLMs demonstrated high accuracy without significant differences (median accuracy scores: OpenAI o1 = 4.5, DeepSeek-R1 = 5; p = 0.5243). However, DeepSeek-R1 significantly outperformed OpenAI o1 in comprehensiveness (p < 0.0001), logical coherence (p = 0.0001), and error types and handling (p = 0.0149). DeepSeek-R1 generated more detailed responses (word count: 110 ± 40 vs. 57 ± 24, p = 0.0001), with longer response times (25 ± 10s vs. 7 ± 4s, p < 0.0001).

Conclusion

DeepSeek-R1 and OpenAI o1 both offer high accuracy for CRC screening guidance, with DeepSeek-R1 providing more comprehensive responses with logically more coherent, and robust error-handling reasoning process, compared with OpenAI o1. Context-specific evaluation is critical for practical clinical integration.