The provision of up-to-date medical information on digital technology and AI systems in journals, clinical practices, and textbooks informing radiologists about patient care has resulted in faster, more reliable, and cheaper image interpretation. This study reviews 27 articles regarding the application of digital technology and artificial intelligence (AI) in radiological scholarship, looking at the incorporation of electronic health system records, digital radiology imaging databases, IT environments, and machine learning—the latter of which has emerged as the most popular AI approach in modern medicine. This article examines the emerging picture surrounding archiving and communication systems in the implementation phase of AI technologies. It explores the most appropriate clinical requirements for the use of AI systems in practice. Continued development in the integration of automated systems, probing the use of information systems, databases, and records, should result in further progress in radiological theory and practice.
References
[1]
Atkinson, R.D. (2016) “It’s Going to Kill Us!” And Other Myths about the Future of Artificial Intelligence. Information Technology & Innovation Foundation.
http://www2.itif.org/2016-myths-machine-learning.pdf
[2]
Wong, S.H., Al-Hasani, H., Alam, Z. and Alam, A. (2019) Artificial Intelligence in Radiology: How Will We Be Affected? European Radiology, 29, 141-143.
https://doi.org/10.1007/s00330-018-5644-3
[3]
Mesko, B. (2017) The Role of Artificial Intelligence in Precision Medicine. Expert Review of Precision Medicine and Drug Development, 2, 239-241.
https://doi.org/10.1080/23808993.2017.1380516
[4]
Liew, C. (2018) The Future of Radiology Augmented with Artificial Intelligence: A Strategy for Success. European Journal of Radiology, 102, 152-156.
https://doi.org/10.1016/j.ejrad.2018.03.019
[5]
Kahn Jr., C.E. (1994) Artificial Intelligence in Radiology: Decision Support Systems. Radiographics, 14, 849-861. https://doi.org/10.1148/radiographics.14.4.7938772
[6]
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.H. and Aerts, H.J. (2018) Artificial Intelligence in Radiology. Nature Reviews Cancer, 18, 500-510.
https://doi.org/10.1038/s41568-018-0016-5
[7]
Fazal, M.I., Patel, M.E., Tye, J. and Gupta, Y. (2018) The Past, Present and Future Role of Artificial Intelligence in Imaging. European Journal of Radiology, 105, 246-250. https://doi.org/10.1016/j.ejrad.2018.06.020
[8]
Schmidt, R. and Gierl, L. (2000) Case-Based Reasoning for Medical Knowledge-Based Systems. Studies in Health Technology and Informatics, 77, 720-725.
[9]
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H. and Wang, Y. (2017) Artificial Intelligence in Healthcare: Past, Present and Future. Stroke and Vascular Neurology, 2, 230-243.
https://doi.org/10.1136/svn-2017-000101
[10]
Garland, L.H. (1949) On the Scientific Evaluation of Diagnostic Procedures: Presidential Address Thirty-Fourth Annual Meeting of the Radiological Society of North America. Radiology, 52, 309-328. https://doi.org/10.1148/52.3.309
[11]
Brady, A.P. (2017) Error and Discrepancy in Radiology: Inevitable or Avoidable? Insights into Imaging, 8, 171-182. https://doi.org/10.1007/s13244-016-0534-1
[12]
Ringler, M.D., Goss, B.C. and Bartholmai, B.J. (2017) Syntactic and Semantic Errors in Radiology Reports Associated with Speech Recognition Software. Health Informatics Journal, 23, 3-13. https://doi.org/10.1177/1460458215613614
[13]
Motyer, R.E., Liddy, S., Torreggiani, W.C. and Buckley, O. (2016) Frequency and Analysis of Non-Clinical Errors Made in Radiology Reports Using the National Integrated Medical Imaging System Voice Recognition Dictation Software. Irish Journal of Medical Science (1971-), 185, 921-927.
https://doi.org/10.1007/s11845-016-1507-6
[14]
Waite, S., Scott, J.M., Legasto, A., Kolla, S., Gale, B. and Krupinski, E.A. (2017) Systemic Error in Radiology. American Journal of Roentgenology, 209, 629-639.
https://doi.org/10.2214/AJR.16.17719
[15]
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G. and The PRISMA Group (2009) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Annals of Internal Medicine, 151, 264-269.
https://doi.org/10.7326/0003-4819-151-4-200908180-00135
[16]
Cuschieri, S. (2019) The STROBE Guidelines. Saudi Journal of Anaesthesia, 13, S31.
https://doi.org/10.4103/sja.SJA_543_18
[17]
Grayev, A. (2019) Artificial Intelligence in Radiology: Resident Recruitment Help or Hindrance? Academic Radiology, 26, 699-700.
https://doi.org/10.1016/j.acra.2019.01.005
[18]
Ting, D.S., Liu, Y., Burlina, P., Xu, X., Bressler, N.M. and Wong, T.Y. (2018) AI for Medical Imaging Goes Deep. Nature Medicine, 24, 539-540.
https://doi.org/10.1038/s41591-018-0029-3
[19]
Bluemke, D.A. (2018) Radiology: Responding to Rapid Changes in Our Field. Radiological Society of North America, Oak Brook, 288.
https://doi.org/10.1148/radiol.2018184013
[20]
Prevedello, L.M., Halabi, S.S., Shih, G., Wu, C.C., Kohli, M.D., Chokshi, F.H., Erickson, B.J., Kalpathy-Cramer, J., Andriole, K.P. and Flanders, A.E. (2019) Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions. Radiology: Artificial Intelligence, 1, e180031.
https://doi.org/10.1148/ryai.2019180031
[21]
Deyer, T. and Doshi, A. (2019) Application of Artificial Intelligence to Radiology. Annals of Translational Medicine, 7, 230. https://doi.org/10.21037/atm.2019.05.79
[22]
Schier, R. (2018) Artificial Intelligence and the Practice of Radiology: An Alternative View. Journal of the American College of Radiology, 15, 1004-1007.
https://doi.org/10.1016/j.jacr.2018.03.046
[23]
Stivaros, S.M., Gledson, A., Nenadic, G., Zeng, X.J., Keane, J. and Jackson, A. (2010) Decision Support Systems for Clinical Radiological Practice—Towards the Next Generation. The British Journal of Radiology, 83, 904-914.
https://doi.org/10.1259/bjr/33620087
[24]
Porto Pazos, A.B. and Pazos Sierra, A. (2008) Advancing Artificial Intelligence through Biological Process Applications. In: Medical Information Science Reference, IGI Global, New York, 231-249. https://doi.org/10.4018/978-1-59904-996-0
[25]
Pesapane, F., Codari, M. and Sardanelli, F. (2018) Artificial Intelligence in Medical Imaging: Threat or Opportunity? Radiologists Again at the Forefront of Innovation in Medicine. European Radiology Experimental, 2, 35.
https://doi.org/10.1186/s41747-018-0061-6
[26]
Kamar, E. (2016) Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence. Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, 9-15 July 2016, 4070-4073.
[27]
Fiesch, M. (2013) Artificial Intelligence in Medicine: Expert Systems. Springer, Berlin.
[28]
Danforth, D.R., Procter, M., Chen, R., Johnson, M. and Heller, R. (2009) Development of Virtual Patient Simulations for Medical Education. 3D Virtual Worlds for Health and Healthcare Journal of Virtual Worlds Research, 2.
https://doi.org/10.4101/jvwr.v2i2.707
[29]
Thrall, J.H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K. and Brink, J. (2018) Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. Journal of the American College of Radiology, 15, 504-508.
https://doi.org/10.1016/j.jacr.2017.12.026
[30]
Gillies, R.J., Kinahan, P.E. and Hricak, H. (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278, 563-577.
https://doi.org/10.1148/radiol.2015151169
[31]
Prevedello, L.M., Erdal, B.S., Ryu, J.L., Little, K.J., Demirer, M., Qian, S. and White, R.D. (2017) Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging. Radiology, 285, 923-931.
https://doi.org/10.1148/radiol.2017162664
[32]
Hanson, C.W. and Marshall, B.E. (2001) Artificial Intelligence Applications in the Intensive Care Unit. Critical Care Medicine, 29, 427-435.
https://doi.org/10.1097/00003246-200102000-00038
[33]
Kulikowski, C.A. (1988) Artificial Intelligence in Medical Consultation Systems: A Review. IEEE Engineering in Medicine and Biology Magazine, 7, 34-39.
https://doi.org/10.1109/51.1972
[34]
Winsberg, F., Elkin, M., Macy Jr., J., Bordaz, V. and Weymouth, W. (1967) Detection of Radiographic Abnormalities in Mammograms by Means of Optical Scanning and Computer Analysis. Radiology, 89, 211-215.
https://doi.org/10.1148/89.2.211
