ChatGPT与DeepSeek在头颈癌分期和治疗计划中的比较:基于指南的研究

IF 2.2
Burcu Vural Camalan, Sumeyra Doluoglu, Nazlim Hilal Taraf, Mehmet Murat Gunay, Samet Ozlugedik
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引用次数: 0

摘要

目的:本前瞻性模拟研究旨在评价和比较ChatGPT(2001年,2023年)和DeepSeek (V3年,2024年)在头颈癌分期和治疗计划中的性能。方法:本前瞻性模拟研究于2025年3月进行,评估和比较两种先进的人工智能(AI)模型ChatGPT(01, 2023)和DeepSeek (V3, 2024)在头颈部恶性肿瘤临床决策中的表现。两位耳鼻喉科医生根据国家综合癌症网络®(NCCN®)2.2025版指南,用英语精心设计了总共50个假设的、基于指南的临床场景。结果:在治疗计划性能的整体分析中,DeepSeek (V3, 2024)与ChatGPT(01, 2023)相比,在统计学上表现出更高的准确性(p = 0.04)。两种模型在肿瘤分期方面表现相当(p = 0.83)。在分期准确性方面,DeepSeek (p = 0.0001)和ChatGPT (p = 0.02)在统计学上都是成功的,并且在治疗主题上提供了完全正确的答案。结论:尽管DeepSeek V3在头颈部肿瘤临床决策支持方面表现出了良好的能力,但这些人工智能工具不能取代多学科肿瘤委员会。然而,它们可以通过快速组织患者数据来显著简化临床工作流程,从而提高董事会效率。未来的努力应优先发展和整合安全的、特定于机构的、适合肿瘤决策的本地大语言模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ChatGPT versus DeepSeek in head and neck cancer staging and treatment planning: guideline-based study.

Purpose: This prospective simulation study was conducted to evaluate and compare the performance of ChatGPT (o1, 2023) and DeepSeek (V3, 2024) in staging and treatment planning for head and neck cancers.

Methods: This prospective simulation study was conducted in March 2025 to evaluate and compare the performance of two advanced artificial intelligence (AI) models, ChatGPT (o1, 2023) and DeepSeek (V3, 2024), in clinical decision-making for head and neck malignancies. A total of 50 hypothetical, guideline-based clinical scenarios were carefully designed in English by two otorhinolaryngologists in alignment with the National Comprehensive Cancer Network® (NCCN®) Guidelines Version 2.2025.

Results: In the overall analysis of treatment planning performance, DeepSeek (V3, 2024) demonstrated statistically superior accuracy compared to ChatGPT (o1, 2023) (p = 0.04). Both models showed comparable performance in tumor staging (p = 0.83). Both DeepSeek (p = 0.0001) and ChatGPT (p = 0.02) were statistically successful in respect of staging accuracy and providing fully correct answers on the subject of treatment.

Conclusion: Although DeepSeek V3 demonstrated promising capability for clinical decision support in head and neck oncology, these artificial intelligence tools cannot replace multidisciplinary tumor boards. However, they can significantly streamline clinical workflows by rapidly organizing patient data, thereby enhancing board efficiency. Future efforts should prioritize the development and integration of secure, institution-specific, local large language models tailored for oncological decision-making.

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