Radiology-artificial Intelligence

Radiology-artificial Intelligence

放射科-人工智能

  • 1区 中科院分区
  • Q1 JCR分区

期刊简介

《Radiology-artificial Intelligence》是由Radiological Society of North America Inc.出版社创办的英文国际期刊(ISSN: 2638-6100),该期刊长期致力于计算机:人工智能领域的创新研究,主要研究方向为Multiple。作为SCIE收录期刊(JCR分区 Q1,中科院 1区),本刊采用OA未开放获取模式以发表计算机:人工智能领域等方向的原创性研究为核心(研究类文章占比98.44%%)。凭借严格的同行评审与高效编辑流程,期刊年载文量精选控制在64篇,确保学术质量与前沿性。成果覆盖Web of Science、Scopus等国际权威数据库,为学者提供推动医学领域高水平交流平台。

投稿咨询

投稿提示

Radiology-artificial Intelligence该刊近年未被列入国际预警名单,年发文量约64篇,录用竞争适中,主题需确保紧密契合医学前沿。投稿策略提示:避开学术会议旺季投稿以缩短周期,语言建议专业润色提升可读性。

  • 医学 大类学科
  • 是否预警
  • SCIE 期刊收录
  • 64 发文量

中科院分区

《新锐期刊分区表》(2026年3月发布)

Top期刊 综述期刊 大类学科 小类学科
医学
1区
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING 核医学
1区 1区

期刊分区表(2025年3月升级版)

Top期刊 综述期刊 大类学科 小类学科
医学
1区
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING 核医学
1区 2区

JCR分区

2025-2026年最新版

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 5 / 210

97.9

学科:RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING SCIE Q1 1 / 217

99.8

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 7 / 210

96.9

学科:RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING SCIE Q1 3 / 217

98.85

2023-2024年最新版

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ESCI Q1 21 / 197

89.6

学科:RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING ESCI Q1 9 / 204

95.8

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ESCI Q1 20 / 198

90.15

学科:RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING ESCI Q1 12 / 204

94.36

CiteScore(2026年6月最新版)

CiteScore SJR SNIP CiteScore 排名
CiteScore:21.1 SJR:4.61 SNIP:4.468
学科类别 分区 排名 百分位
大类:Medicine 小类:Radiology, Nuclear Medicine and Imaging Q1 4 / 361

99%

大类:Medicine 小类:Radiological and Ultrasound Technology Q1 2 / 64

97%

大类:Medicine 小类:Artificial Intelligence Q1 26 / 570

95%

期刊发文

  • Quantifying the Privacy-Utility Trade-off in Medical Imaging A

    Author: Yu, Zekai

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 2, pp. -. DOI: 10.1148/ryai.251044

  • Quantifying the Privacy-Utility Trade-off in Medical Imaging AI The Respons

    Author: Yu, Zekai

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 2, pp. -. DOI:

  • Adnexal Lesion Discrimination Using Deep Learning Analysis of Dynamic Contrast-enhanced US Image

    Author: Wu, Manli; Yang, Hong; Chen, Ying; Wu, Shuangyu; Liang, Tianming; Zhang, Man; Qu, Enze; Sun, Xiaofeng; Zhang, Rui; Mu, Liang; Xiao, Li; Wen, Hong; Wang, Ruili; Liu, Tingting; Meng, Xiaotao; Su, Manting; Wang, Ying; Gu, Jian; Chen, Sijia; Wang, Yaping; Zhao, Qinghong; Liu, Juan; Cheng, Ping; Wang, Ruixuan; Hu, Jianfang; Zhang, Xinling

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.240786

  • Deep Learning for Coronary Stenosis Detection in Heavily Calcified Plaques at Coronary CT Angiography: A Stepwise, Multicenter Stud

    Author: Wang, Rui; Wang, Siwen; Zhang, Libo; Schoepf, U. Joseph; Zhang, Fandong; Chen, Wei; Zhou, Zhen; Fang, Zhe; Hu, Bin; Yu, Yizhou; Zhang, Jiayin; Wang, Ximing; Zhang, Longjiang; Xu, Lei

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.250109

  • Visualizing Radiologic Connections: An Explainable Coarse-to-Fine Foundation Modelwith Multiview Mammograms and Associated Report

    Author: Gao, Yuan; Zhou, Hong-Yu; Wang, Xin; Portaluri, Antonio; Zhang, Tianyu; Beets-Tan, Regina; Han, Luyi; Lu, Chunyao; Estacio, Laura; Da'ngelo, Anna; Ursprung, Stephan; Yu, Yizhou; Teuwen, Jonas; Tan, Tao; Mann, Ritse

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.240646

  • Gastric Neoplasm Detection at Contrast-enhanced CT with Deep Learnin

    Author: Chen, Xin; Xia, Yingda; Yao, Lisha; Li, Suyun; Liang, Yanting; Zheng, Zhilin; Yuan, Mingze; Yao, Jiawen; Zhang, Ruiping; Tu, Wenting; Guo, Yongmei; Liang, Dan; Ma, Zelan; Chen, Dandan; Lai, Lisha; Xie, Xiaowen; Yu, Yifan; Jia, Yanlian; Zhang, Ling; Liu, Zaiyi

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.250145

  • Pseudo-Contrast-enhanced US via Enhanced Generative Adversarial Networks for Evaluating Tumor Ablation Efficac

    Author: Chen, Chen; Yu, Jiabin; Xu, Zhikang; Xu, Changsong; Zhou, Zubang; Hao, Jindong; Wang, Vicky Yang; Yao, Jincao; Zhou, Lingyan; Xu, Chenke; Song, Mei; Zhang, Qi; Liu, Xiaofang; Sui, Lin; Yan, Yuqi; Jiang, Tian; Zhou, Yahan; Wu, Yingtianqi; Xiao, Binggang; Xu, Chenjie; Mi, Hongmei; Yang, Li; Wu, Zhiwei; He, Qingquan; Chen, Jian; Liu, Qi; Xu, Don

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2025; Vol. 7, Issue 3, pp. -. DOI: 10.1148/ryai.240370

  • Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosi

    Author: Tang, Tianyu; Cui, Ying; Lu, Chunqiang; Li, Huiming; Zhou, Jiaying; Zhang, Xiaoyu; Zhou, Yujie; Zhang, Ying; Zhang, Yi; Xu, Yuhao; Li, Yuefeng; Ju, Shenghong

    Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2025; Vol. 7, Issue 3, pp. -. DOI: 10.1148/ryai.240072