Artificial intelligence in diagnostic imaging: Impact on the radiography profession.

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.

[1]  E. Denton,et al.  Radiographer reporting: A literature review to support cancer workforce planning in England. , 2019, Radiography.

[2]  E. Mendelson,et al.  Artificial Intelligence in Breast Imaging: Potentials and Limitations. , 2019, AJR. American journal of roentgenology.

[3]  Walter Plasencia,et al.  Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning. , 2018, JCI insight.

[4]  Felix Nensa,et al.  Artificial Intelligence in Nuclear Medicine , 2019, The Journal of Nuclear Medicine.

[5]  R Price,et al.  Re-Engineering the Soft Machine: The Impact of Developing Technology and Changing Practice on Diagnostic Radiographer Skill Requirements , 2000, Health services management research.

[6]  R. Winder,et al.  Variation in radiographic protocols in paediatric interventional cardiology , 2013, Journal of radiological protection : official journal of the Society for Radiological Protection.

[7]  Elmar Kotter,et al.  Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists , 2019, Artificial Intelligence in Medical Imaging.

[8]  Nuo Tong,et al.  Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks , 2018, Medical physics.

[9]  C. Hayre,et al.  Image acquisition in general radiography: The utilisation of DDR. , 2017, Radiography.

[10]  In-So Kweon,et al.  Learning a Deep Convolutional Network for Light-Field Image Super-Resolution , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[11]  Leslie Ying,et al.  Accelerating magnetic resonance imaging via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[12]  Hersh Chandarana,et al.  Automated image quality evaluation of T2‐weighted liver MRI utilizing deep learning architecture , 2018, Journal of magnetic resonance imaging : JMRI.

[13]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[14]  T. Zhuang,et al.  An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction , 2019, The Lancet Digital Health.

[15]  David A Bluemke,et al.  Radiology in 2018: Are You Working with AI or Being Replaced by AI? , 2018, Radiology.

[16]  Dong Ni,et al.  Deep Learning in Medical Ultrasound Analysis: A Review , 2019, Engineering.

[17]  Bradley J. Erickson,et al.  Extraction of brain tissue from CT head images using fully convolutional neural networks , 2018, Medical Imaging.

[18]  Zi-Ping Li,et al.  An Individually Optimized Protocol of Contrast Medium Injection in Enhanced CT Scan for Liver Imaging , 2017, Contrast media & molecular imaging.

[19]  Andrew D. Brown,et al.  Using machine learning for sequence-level automated MRI protocol selection in neuroradiology , 2018, J. Am. Medical Informatics Assoc..

[20]  Krzysztof J. Geras,et al.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. , 2019, Radiology.

[21]  J. French,et al.  Preparing for Artificial Intelligence: Systems-Level Implications for the Medical Imaging and Radiation Therapy Professions. , 2019, Journal of medical imaging and radiation sciences.

[22]  Kenji Suzuki,et al.  Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing , 2018, Medical Imaging.

[23]  John K Field,et al.  The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial , 2017, European Radiology.

[24]  J. Goo,et al.  The Potential Role of Grid-Like Software in Bedside Chest Radiography in Improving Image Quality and Dose Reduction: An Observer Preference Study , 2018, Korean journal of radiology.

[25]  Yaozong Gao,et al.  CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation , 2019, Medical Image Anal..

[26]  N. Spencer Re: Can radiographers read screening mammograms? , 2003, Clinical radiology.

[27]  J. Yielder,et al.  Where radiographers fear to tread: Resistance and apathy in radiography practice , 2009 .

[28]  E. Pakdemirli Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading? , 2019, Acta radiologica open.

[29]  B. van Ginneken,et al.  Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT Scans , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[30]  O. Bouamra,et al.  The use of whole-body computed tomography in major trauma: variations in practice in UK trauma hospitals , 2017, Emergency Medicine Journal.

[31]  Kuan-Ta Chen,et al.  Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning , 2019, npj Digital Medicine.

[32]  D. Akroyd,et al.  Occupational burnout among radiation therapists in Australia: Findings from a mixed methods study. , 2017, Radiography.

[33]  A. McMillan,et al.  Deep learning Mr imaging–based attenuation correction for PeT/Mr imaging 1 , 2017 .

[34]  A. Syed,et al.  Artificial Intelligence in Radiology: Current Technology and Future Directions , 2018, Seminars in Musculoskeletal Radiology.

[35]  Yaozong Gao,et al.  Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks , 2016, LABELS/DLMIA@MICCAI.

[36]  T. Suominen,et al.  Workplace culture assessed by radiographers in Finland. , 2019, Radiography.

[37]  W. Teeuwisse,et al.  An inter-hospital comparison of patient dose based on clinical indications , 2007, European Radiology.

[38]  James H Thrall,et al.  Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. , 2018, Journal of the American College of Radiology : JACR.

[39]  C. Beardmore,et al.  Audit of the job satisfaction levels of the UK radiography and physics workforce in UK radiotherapy centres 2012. , 2014, The British journal of radiology.

[40]  Luciano M Prevedello,et al.  Machine Learning in Radiology: Applications Beyond Image Interpretation. , 2017, Journal of the American College of Radiology : JACR.

[41]  Dong Ni,et al.  FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks , 2017, IEEE Transactions on Cybernetics.

[42]  M. McEntee,et al.  Establishment of CT diagnostic reference levels in Ireland. , 2012, The British journal of radiology.

[43]  J. Hawnaur,et al.  Diagnostic radiology , 1999, BMJ.

[44]  X. Cui,et al.  Artificial intelligence in breast ultrasound , 2019, World journal of radiology.

[45]  Dong Si,et al.  Comparison of deep learning approaches to low dose CT using low intensity and sparse view data , 2019, Medical Imaging.

[46]  Artificial intelligence and nuclear medicine , 2018, Nuclear medicine communications.

[47]  Erwin W Hans,et al.  Reducing the throughput time of the diagnostic track involving CT scanning with computer simulation. , 2012, European journal of radiology.

[48]  Eliot L Siegel,et al.  Technologists' productivity when using PACS: comparison of film-based versus filmless radiography. , 2002, AJR. American journal of roentgenology.