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科学与医疗

医学 AI

医学智能、临床 AI、医学影像、病理、诊断和医疗健康大模型。

2026-03-24 至 2026-03-24 共收录 7 信号源:cs.CV, cs.LG, q-bio, eess.IV, eess.SP

1. 医学数据与评测 7 篇

2603.21656 2026-03-24 cs.LG cs.CY 83%

TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints

TrustFed: 在数据隐私约束下实现可信的医疗AI

Vagish Kumar, Syed Bahauddin Alam, Souvik Chakraborty

机构 * Department of Applied Mechanics(应用力学系) Indian Institute of Technology Delhi(印度理工学院德里) Grainger College of Engineering(工程学院) Nuclear, Plasma & Radiological Engineering Department(核物理与辐射工程系) National Center for Supercomputing Applications(国家超级计算应用中心) Yardi School of Artificial Intelligence (ScAI)(Yardi人工智能学院(ScAI)) University of Illinois Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校)

专题命中 医学数据与评测 :medical AI(title);healthcare AI(abstract);medical image(abstract);分类 cs.LG

AI总结 TrustFed通过联邦不确定性量化框架,在异构和不平衡医疗数据上提供分布无关的有限样本覆盖保证,无需集中访问,提升预测可靠性。

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2603.21669 2026-03-24 cs.RO cs.CV 57%

PRM-as-a-Judge: A Dense Evaluation Paradigm for Fine-Grained Robotic Auditing

PRM-as-a-Judge:细粒度机器人审计的密集评估范式

Yuheng Ji, Yuyang Liu, Huajie Tan, Xuchuan Huang, Fanding Huang, Yijie Xu, Cheng Chi, Yuting Zhao, Huaihai Lyu, Peterson Co, Mingyu Cao, Qiongyu Zhang, Zhe Li, Enshen Zhou, Pengwei Wang, Zhongyuan Wang, Shanghang Zhang, Xiaolong Zheng

机构 * State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences(多模态人工智能系统国家重点实验室,自动化研究所,中国科学院) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University(多媒体信息处理国家重点实验室,计算机科学系,北京大学) Beijing Academy of Artificial Intelligence(北京人工智能研究院) Tsinghua University(清华大学) University of Sydney(悉尼大学) Beihang University(北航大学)

专题命中 医学数据与评测 :diagnosis(abstract);分类 cs.CV

AI总结 本文提出PRM-as-a-Judge方法,通过过程奖励模型对轨迹视频进行密集评估,引入OPD指标系统,验证了宏一致性与微分辨率特性,揭示了主流策略在长周期任务中的行为特征与失败模式。

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2603.21660 2026-03-24 cs.CV 57%

OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging

OmniFM:迈向模态鲁棒且任务无关的联邦学习 for 异质医学影像

Meilin Liu, Jiaying Wang, Jing Shan

机构 * School of Software, Shenyang University of Technology(沈阳理工大学软件学院)

专题命中 医学数据与评测 :medical image(abstract);分类 cs.CV

AI总结 OmniFM提出一种统一训练分类、分割、超分辨率、视觉问答和多模态融合的联邦学习框架,通过频域洞察提升跨模态一致性,实现任务无关和模态鲁棒的联邦学习。

Comments Accepted by CVPR 2026 (Main)

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2603.21251 2026-03-24 cs.NI eess.SP 57%

WirelessBench: A Tolerance-Aware LLM Agent Benchmark for Wireless Network Intelligence

WirelessBench: 一种面向无线网络智能的容忍感知LLM代理基准

Jingwen Tong, Fang Liu, Linkai Xv, Shiliang Lu, Kangqi Li, Yiqian Zhang, Yijie Song, Zeyang Xue, Jun Zhang

专题命中 医学数据与评测 :diagnosis(abstract);分类 eess.SP

AI总结 WirelessBench提出一种容忍感知的LLM无线代理基准,通过三层认知体系和三个设计原则,解决现有基准在连续故障和灾难性错误检测上的不足,提升无线网络管理的自主性。

Comments This paper is submitted to a possiable journal for publication

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2603.20345 2026-03-24 q-bio.QM stat.AP 54%

Towards Improved Short-term Hypoglycemia Prediction and Diabetes Management based on Refined Heart Rate Data

基于精细化心率数据的短期低血糖预测与糖尿病管理改进研究

Vaibhav Gupta, Florian Grensing, Beyza Cinar, Louisa van den Boom, Maria Maleshkova

专题命中 医学数据与评测 :diagnosis(abstract);分类 q-bio

AI总结 本文提出CRBC和CMPV两种新方法,用于填补短期心率数据缺失,提升低血糖预测准确性,并通过综合评估指标验证了其有效性。

Comments 10 pages, 2 tables

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2512.23743 2026-03-24 cs.SE cs.AI 50%

Hybrid-Code v2: Zero-Hallucination Clinical ICD-10 Coding via Neuro-Symbolic Verification and Automated Knowledge Base Expansion

Hybrid-Code v2:通过神经符号验证和自动知识库扩展实现零幻觉的临床ICD-10编码

Yunguo Yu

机构 * AI Innovation & Prototyping, Zyter(人工智能创新与原型开发,Zyter)

专题命中 医学数据与评测 :medical AI(abstract)

AI总结 Hybrid-Code v2结合神经网络与符号验证,实现零类型I幻觉,同时保持高覆盖率和精度,通过自动知识库扩展解决规则系统扩展性问题。

Comments Version 2: Substantially extended version with (1) multi-layer verification framework (format, evidence, negation, temporal, exclusion), (2) automated knowledge base expansion from unlabeled clinical text, (3) formal zero Type-I hallucination guarantees, and (4) expanded experimental evaluation on 5,000 cases with detailed error analysis. 28 pages, 3 figure, original research paper;

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2412.02868 2026-03-24 cs.AI 50%

PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs

PrecLLM: 一种用于从非结构化电子健康记录中高效提取临床注释的隐私保护框架,使用小型语言模型

Yixiang Qu, Yifan Dai, Shilin Yu, Pradham Tanikella, Malvika Pillai, Walter Chen, Jialiu Xie, Yishan Ren, Duan Wang, Yikai Wang, Sid Sheth, Guanting Chen, Yufeng Liu, Travis Schrank, Trevor Hackman, Didong Li, Di Wu

机构 * Department of Biostatistics, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校生物统计学系) Department of Genetics, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校遗传学系) Curriculum for Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校生物信息学与计算生物学课程) Carolina Health Informatics Program, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校健康信息学计划) Department of Statistics and Operations Research, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校统计学与运筹学系) Department of Otolaryngology/Head and Neck Surgery, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校耳鼻喉科及头颈外科系) Department of Statistics, University of Michigan(密歇根大学统计学系) Department of Biomedical Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校阿德姆牙科学院生物医学科学系) Computational Medicine Program, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校计算医学计划) Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校林伯格综合癌症中心)

专题命中 医学数据与评测 :clinical LLM(abstract)

AI总结 本文提出PrecLLM框架,利用小型语言模型高效处理非结构化电子健康记录,通过正则表达式和RAG技术提升隐私保护下的临床注释提取性能。

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