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预先培训的ChatGPT用于自动化微生物识别和抗生素敏感性测试系统的报告生成

Liping Hu¹, Xiaohong Xu², Yingting Zhuang³

¹Department of Laboratory Medicine, Xiamen Chang Gung Hospital Hua Qiao University, Xiamen, People's Republic of China.

Scientific reports

|October 16, 2025

概括

像ChatGPT这样的大型语言模型可以改进自动微生物识别和抗生素敏感性测试 (ID/AST) 报告. 提示工程显著增强了ChatGPT.

科学领域:

临床微生物学临床微生物学
医疗保健中的人工智能
医疗信息学医疗信息学

背景情况:

自动化微生物识别和抗生素敏感性测试 (ID/AST) 系统在临床微生物学中至关重要.
从这些系统中标准化报告生成仍然是一个重大挑战.
大型语言模型 (LLM) 提供了提高报告一致性和客观性的潜力.

研究的目的:

评估ChatGPT在为自动化ID/AST系统生成标准化微生物学报告方面的有效性.
将人工智能生成的报告与临床微生物学家 (CM) 制作的报告进行比较.
评估结构化提示工程对AI报告质量的影响.

主要方法:

根据临床和实验室标准研究所 (CLSI) 的指导方针,为ChatGPT开发了一个快速的工程框架.
分析了8个临床病例,比较了CM,快速训练前的ChatGPT (ChatGPT_BT) 和快速训练后的ChatGPT (ChatGPT_AT) 的报告.
绩效被评估在五个方面:准确性,相关性,客观性,完整性和清晰性.

主要成果:

聊天GPT_BT显示的相关性和完整性高于CM (p < 0.0001).
与CM相比,ChatGPT_AT在所有五个维度都表现出显著的改善 (p < 0.001).
聊天GPT_AT在相关性,客观性,完整性和清晰度方面也比聊天GPT_BT显著改善 (p < 0.05).

结论:

结构化提示工程显著提高了ChatGPT生成高质量的微生物学报告的能力.
聊天GPT显示了作为临床微生物学家的辅助工具的巨大潜力.
人工智能辅助的报告可以提高自动化ID/AST系统报告的客观性,清晰性,完整性,相关性和准确性.

关键词:

自动化的ID/AST系统.聊天GPT 聊天在GPT 聊天临床微生物学家临床微生物学家法学士 (LLM) 是一个专业.

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