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Diagnostic accuracy of DeepSeek-R1 and ChatGPT-4o in emergency patients:A comparative study 认领 引用
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作者 Xiangyue Jiang Yu Zhou +3 位作者 Zhiyu Gong Yanan Gu Na Li Qingli Dou 《Journal of Acute Disease》 2025年第12期1-7,共7页
Objective:To compare the diagnostic performance of DeepSeek-R1 and ChatGPT-4o in emergency department inpatients and explore their clinical practical value.Methods:A retrospective study was conducted using clinical da... Objective:To compare the diagnostic performance of DeepSeek-R1 and ChatGPT-4o in emergency department inpatients and explore their clinical practical value.Methods:A retrospective study was conducted using clinical data from emergency department inpatients discharged in December 2024.Discharge diagnoses served as the gold standard.Patient data(age,symptoms,exams,tests)were input into DeepSeek-R1 and GPT-4o with the prompt:“What is the most likely diagnosis?”Two physicians scored outputs(0-3)to assess accuracy and consistency.Results:A total of 328 cases were analyzed.The mean scores for DeepSeek-R1 and ChatGPT-4o were 2.33±1.07 and 2.32±1.05,respectively,with no statistically significant difference(P=0.82).The Z-score was-0.232,indicating highly similar performance between the two models.However,the rate of accurate diagnoses was 66.5%.Diagnostic performance declined with increasing patient age.Conclusions:DeepSeek-R1 and ChatGPT-4o demonstrated comparable diagnostic performance in emergency department settings,but the misdiagnosis risk remained high.Both models can serve as auxiliary tools to expand physicians'diagnostic considerations but should be integrated with clinical expertise for comprehensive judgment. 展开更多
关键词 Large language models DeepSeek-R1 ChatGPT-4o Auxiliary diagnosis Diagnostic accuracy
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Semantic Intelligence in Metallurgy: A Dual-Stage Language Model Framework for Processing-Aware Magnesium Alloy Design 认领 引用
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作者 Ziliang Lu Ishwar Kapoor +6 位作者 Takeru Araki Lu Chen Tao Song Yixiang Li Yang Su Xiaoqin Zeng Leyun Wang 《Materials Genome Engineering Advances》 CAS CSCD 2026年第1期45-56,共12页
Advances in artificial intelligence(AI)and large language models(LLMs)are transforming materials research by enabling automated data extraction,knowledge integration,and property prediction.This study presents a dual-... Advances in artificial intelligence(AI)and large language models(LLMs)are transforming materials research by enabling automated data extraction,knowledge integration,and property prediction.This study presents a dual-stage,LLM-assisted framework for magnesium alloy design that combines semantic extraction,thermodynamic reasoning,and machine learning(ML).Using Qwen-2.5,alloy chemistry,processing details,and thermal and mechanical property data are automatically extracted from full-text literature and converted into structured records.The extracted information is expanded with thermodynamic phase descriptors predicted by DeepSeek-R1 and numerical processing features generated from CLIP-based embeddings.The feature set is optimized using sequential backward selection(SBS),and predictive models are developed using support vector machines(SVM),random forest(RF),and eXtreme Gradient Boosting(XGB).The proposed workflow effectively integrates chemistry,thermodynamics,and processing history,achieving robust predictions for thermal conductivity,yield strength,and ultimate tensile strength.The best performing models yielded R2 values of~0.80(RMSE~9.98 W·m−1 K−1),~0.69(RMSE~37.2 MPa),and~0.73(RMSE~31.5 MPa)for TC,YS,and UTS,respectively.Validation against CALPHAD calculations shows that DeepSeek-R1 reproduces equilibrium phase fractions within 1 wt.%deviation.Overall,this work shows that semantic intelligence can link literature-derived knowledge with predictive modeling,providing a pathway for processing-informed alloy design. 展开更多
关键词 DeepSeek-R1 large language model machine learning magnesium alloys Qwen-2.5 strength thermal conductivity
双重加工视角下的人机协同认知:基于大语言模型的动态认知分工 认领 引用 被引量：2
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作者 陈向东 刘城烨 《远程教育杂志》 CSSCI 北大核心 2026年第3期37-47,93,共11页
本文以双重加工理论为分析视角,探讨推理增强型大语言模型进入教育情境后支持人机协同认知的实现路径。借助测试时计算、强化学习、思维链等机制,大语言模型在教育情境中同时呈现快速生成与分步推理并存的“类系统1/类系统2”加工倾向,... 本文以双重加工理论为分析视角,探讨推理增强型大语言模型进入教育情境后支持人机协同认知的实现路径。借助测试时计算、强化学习、思维链等机制,大语言模型在教育情境中同时呈现快速生成与分步推理并存的“类系统1/类系统2”加工倾向,进而冲击了“人工智能提供信息—人类承担思考”的既有认知责任分工,以及由此产生的认知外包、能力替代与协同冗余等新挑战。本文通过阐明两类加工倾向的功能映射及其教育含义,从协同原则、目标、机制与保障四个维度,构建适配教育情境的人机协同认知模型。该模型以任务结构、学习阶段与认知负荷为依据,对“快答/深推”进行条件化触发、强度约束与阶段性回收,并在交互层面引入元认知机制,支持学习者对人工智能推理过程进行监控、质疑与再加工。在此基础上,本文结合高结构化STEM问题解决、论证性写作、探究式学习与课堂即时诊断等典型教学场景,阐明大语言模型应由答案生成工具转向可解释、可控制、可回收的认知支架。结论表明,人工智能时代的人机协同学习设计,应以促进学习者系统2能力发展为核心目标,在提升任务效率的同时,维护学习者的独立思考、论证反思与迁移应用能力。 展开更多
关键词 大语言模型 双重加工理论 人机协同 系统1/系统2 人机协同认知模型 共享心智模型
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