Artificial intelligence in diagnostic imaging: impact on the radiography profession

被引:114
作者
Hardy, Maryann [1 ]
Harvey, Hugh [2 ]
机构
[1] Univ Bradford, Bradford, W Yorkshire, England
[2] Hardian Hlth, Haywards Heath, England
关键词
RADIOLOGY; QUALITY; TIME;
D O I
10.1259/bjr.20190840
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.
引用
收藏
页数:7
相关论文
共 60 条
  • [1] The Potential Role of Grid-Like Software in Bedside Chest Radiography in Improving Image Quality and Dose Reduction: An Observer Preference Study
    Ahn, Su Yeon
    Chae, Kum Ju
    Goo, Jin Mo
    [J]. KOREAN JOURNAL OF RADIOLOGY, 2018, 19 (03) : 526 - 533
  • [2] Extraction of Brain Tissue from CT Head Images using Fully Convolutional Neural Networks
    Akkus, Zeynettin
    Kostandy, Petro M.
    Philbrick, Kenneth A.
    Erickson, Bradley J.
    [J]. MEDICAL IMAGING 2018: IMAGE PROCESSING, 2018, 10574
  • [3] Annarumma M, 2019, RADIOLOGY, V291, P272, DOI 10.1148/radiol.2019194005
  • [4] Radiology in 2018: Are You Working with AI or Being Replaced by AI?
    Bluemke, David A.
    [J]. RADIOLOGY, 2018, 287 (02) : 365 - 366
  • [5] Using machine learning for sequence-level automated MRI protocol selection in neuroradiology
    Brown, Andrew D.
    Marotta, Thomas R.
    [J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2018, 25 (05) : 568 - 571
  • [6] Care Quality Commission CQC., 2018, Radiology review: a national review of radiology reporting within the NHS in England
  • [7] Radiographer reporting: A literature review to support cancer workforce planning in England
    Culpan, G.
    Culpan, A. -M.
    Docherty, P.
    Denton, E.
    [J]. RADIOGRAPHY, 2019, 25 (02) : 155 - 163
  • [8] Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture
    Esses, Steven J.
    Lu, Xiaoguang
    Zhao, Tiejun
    Shanbhogue, Krishna
    Dane, Bari
    Bruno, Mary
    Chandarana, Hersh
    [J]. JOURNAL OF MAGNETIC RESONANCE IMAGING, 2018, 47 (03) : 723 - 728
  • [9] European Soc Radiology 2009, 2010, INSIGHTS IMAGING, V1, P2, DOI 10.1007/s13244-009-0007-x
  • [10] Research Capacity at Traditional Chinese Medicine (TCM) Centers in China: A Survey of Clinical Investigators
    Feng, Shuo
    Han, Mei
    Lai, Lily
    Wang, Si-cheng
    Liu, Jian-ping
    [J]. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE, 2017, 2017