Skip to main content

Main menu

  • Home
  • Content
    • Latest
    • Archive
    • home
  • Info for
    • Authors
    • Reviewers
    • Subscribers
    • Institutions
    • Advertisers
    • Join SMJ
  • About Us
    • About Us
    • Editorial Office
    • Editorial Board
  • More
    • Advertising
    • Alerts
    • Feedback
    • Folders
    • Help
  • Other Publications
    • NeuroSciences Journal

User menu

  • My alerts
  • Log in

Search

  • Advanced search
Saudi Medical Journal
  • Other Publications
    • NeuroSciences Journal
  • My alerts
  • Log in
Saudi Medical Journal

Advanced Search

  • Home
  • Content
    • Latest
    • Archive
    • home
  • Info for
    • Authors
    • Reviewers
    • Subscribers
    • Institutions
    • Advertisers
    • Join SMJ
  • About Us
    • About Us
    • Editorial Office
    • Editorial Board
  • More
    • Advertising
    • Alerts
    • Feedback
    • Folders
    • Help
  • Follow psmmc on Twitter
  • Visit psmmc on Facebook
  • RSS
Research ArticleOriginal Articles
Open Access

Artificial intelligence in radiology

Are Saudi residents ready, prepared, and knowledgeable?

Mawya A. Khafaji, Mohammed A. Safhi, Roia H. Albadawi, Salma O. Al-Amoudi, Salah S. Shehata and Fadi Toonsi
Saudi Medical Journal January 2022, 43 (1) 53-60; DOI: https://doi.org/10.15537/smj.2022.43.1.20210337
Mawya A. Khafaji
From the Department of Radiology (Khafaji, Toonsi), Faculty of Medicine, King Abdulaziz University, from the Department of Radiodiagnostics and Medical Imaging (Albadawi), King Abdulaziz Medical City, from the Department of Radiodiagnostics and Medical Imaging (Shehata), King Faisal Specialist Hospital and Research Centre, Jeddah, and from the Department of Radiodiagnostics and Medical Imaging (Al-Amoudi, Safhi), Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia.
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
Mohammed A. Safhi
From the Department of Radiology (Khafaji, Toonsi), Faculty of Medicine, King Abdulaziz University, from the Department of Radiodiagnostics and Medical Imaging (Albadawi), King Abdulaziz Medical City, from the Department of Radiodiagnostics and Medical Imaging (Shehata), King Faisal Specialist Hospital and Research Centre, Jeddah, and from the Department of Radiodiagnostics and Medical Imaging (Al-Amoudi, Safhi), Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia.
MBBS
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Roia H. Albadawi
From the Department of Radiology (Khafaji, Toonsi), Faculty of Medicine, King Abdulaziz University, from the Department of Radiodiagnostics and Medical Imaging (Albadawi), King Abdulaziz Medical City, from the Department of Radiodiagnostics and Medical Imaging (Shehata), King Faisal Specialist Hospital and Research Centre, Jeddah, and from the Department of Radiodiagnostics and Medical Imaging (Al-Amoudi, Safhi), Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia.
MBBS
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Salma O. Al-Amoudi
From the Department of Radiology (Khafaji, Toonsi), Faculty of Medicine, King Abdulaziz University, from the Department of Radiodiagnostics and Medical Imaging (Albadawi), King Abdulaziz Medical City, from the Department of Radiodiagnostics and Medical Imaging (Shehata), King Faisal Specialist Hospital and Research Centre, Jeddah, and from the Department of Radiodiagnostics and Medical Imaging (Al-Amoudi, Safhi), Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia.
MBBS
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Salah S. Shehata
From the Department of Radiology (Khafaji, Toonsi), Faculty of Medicine, King Abdulaziz University, from the Department of Radiodiagnostics and Medical Imaging (Albadawi), King Abdulaziz Medical City, from the Department of Radiodiagnostics and Medical Imaging (Shehata), King Faisal Specialist Hospital and Research Centre, Jeddah, and from the Department of Radiodiagnostics and Medical Imaging (Al-Amoudi, Safhi), Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia.
MBBS
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fadi Toonsi
From the Department of Radiology (Khafaji, Toonsi), Faculty of Medicine, King Abdulaziz University, from the Department of Radiodiagnostics and Medical Imaging (Albadawi), King Abdulaziz Medical City, from the Department of Radiodiagnostics and Medical Imaging (Shehata), King Faisal Specialist Hospital and Research Centre, Jeddah, and from the Department of Radiodiagnostics and Medical Imaging (Al-Amoudi, Safhi), Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia.
MBBS, FRCPC.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • eLetters
  • Info & Metrics
  • References
  • PDF
Loading

References

  1. 1.↵
    1. Pinto Dos Santos D,
    2. Giese D,
    3. Brodehl S,
    4. Chon SH,
    5. Staab W,
    6. Kleinert R, et al.
    Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol 2019; 29: 1640–1646.
    OpenUrlCrossRefPubMed
  2. 2.
    1. Kagiyama N,
    2. Shrestha S,
    3. Farjo PD,
    4. Sengupta PP.
    Artificial intelligence: practical primer for clinical research in cardiovascular disease. J Am Heart Assoc 2019; 8: e012788.
    OpenUrlPubMed
  3. 3.↵
    1. Kang J,
    2. Thompson RF,
    3. Aneja S,
    4. Lehman C,
    5. Trister A,
    6. Zou J, et al.
    National Cancer Institute workshop on artificial intelligence in radiation oncology: training the next generation. Pract Radiat Oncol 2021; 11: 74–83.
    OpenUrl
  4. 4.
    1. Miller DD,
    2. Brown EW.
    Artificial intelligence in medical practice: the question to the answer? Am J Med 2018; 131: 129–133.
    OpenUrlCrossRefPubMed
  5. 5.
    1. Patel VL,
    2. Shortliffe EH,
    3. Stefanelli M,
    4. Szolovits P,
    5. Berthold MR,
    6. Bellazzi R, et al.
    The coming of age of artificial intelligence in medicine. Artif Intell Med 2009; 46: 5–17.
    OpenUrlCrossRefPubMedWeb of Science
  6. 6.↵
    1. Stewart J,
    2. Sprivulis P,
    3. Dwivedi G.
    Artificial intelligence and machine learning in emergency medicine. Emerg Med Australas 2018; 30: 870–874.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Krittanawong C,
    2. Zhang H,
    3. Wang Z,
    4. Aydar M,
    5. Kitai T.
    Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 2017; 69: 2657–2664.
    OpenUrlFREE Full Text
  8. 8.↵
    1. Alsharqi M,
    2. Woodward WJ,
    3. Mumith JA,
    4. Markham DC,
    5. Upton R,
    6. Leeson P.
    Artificial intelligence and echocardiography. Echo Res Pract 2018; 5: R115–R125.
    OpenUrl
  9. 9.↵
    1. Shameer K,
    2. Johnson KW,
    3. Glicksberg BS,
    4. Dudley JT,
    5. Sengupta PP.
    Machine learning in cardiovascular medicine: are we there yet? Heart 2018; 104: 1156–1164.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Dreyer KJ,
    2. Geis JR.
    When machines think: radiology’s next frontier. Radiology 2017; 285: 713–718.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Nemati S,
    2. Holder A,
    3. Razmi F,
    4. Stanley MD,
    5. Clifford GD,
    6. Buchman TG.
    An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 2018; 46: 547–553.
    OpenUrlCrossRefPubMed
  12. 12.↵
    The Economist. Automation and anxiety: will smarter machines cause mass unemployment? [Updated 2016; 2020 Oct 25]. Available from: https://www.economist.com/special-report/2016/06/23/automation-and-anxiety
  13. 13.↵
    1. Langlotz CP.
    Will artificial intelligence replace radiologists? Radiol Artif Intell 2019; 1: e190058.
    OpenUrl
  14. 14.↵
    1. Sit C,
    2. Srinivasan R,
    3. Amlani A,
    4. Muthuswamy K,
    5. Azam A,
    6. Monzon L, et al.
    Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging 2020; 11: 14.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Gong B,
    2. Nugent JP,
    3. Guest W,
    4. Parker W,
    5. Chang PJ,
    6. Khosa F, et al.
    Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study. Acad Radiol 2019; 26: 566–577.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Collado-Mesa F,
    2. Alvarez E,
    3. Arheart K.
    The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol 2018; 15: 1753–1757.
    OpenUrlPubMed
  17. 17.↵
    1. Codari M,
    2. Melazzini L,
    3. Morozov SP,
    4. Van Kuijk CC,
    5. Sconfienza LM,
    6. Sardanelli F.
    Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 2019; 10: 105.
    OpenUrlCrossRefPubMed
  18. 18.↵
    Saudi Commission for Health Specialties. Saudi Board: medical imaging curriculum. [Updated 2015; 2020 Oct 25]. Available from: https://www.scfhs.org.sa/MESPS/TrainingProgs/TrainingProgsStatement/Documents/Medical%20Imaging%20new.pdf
  19. 19.↵
    Raosoft. Sample size calculator. [Updated 2004; 2020 Apr 10]. Available from: http://www.raosoft.com/samplesize.html
  20. 20.↵
    1. Oh S,
    2. Kim JH,
    3. Choi SW,
    4. Lee HJ,
    5. Hong J,
    6. Kwon SH.
    Physician confidence in artificial intelligence: an online mobile survey. J Med Internet Res 2019; 21: e12422.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Van Hoek J,
    2. Huber A,
    3. Leichtle A,
    4. Härmä K,
    5. Hilt D,
    6. von Tengg-Kobligk H, et al.
    A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol 2019; 121: 108742.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Carlos RC,
    2. Kahn CE,
    3. Halabi S.
    Data science: big data, machine learning, and artificial intelligence. J Am Coll Radiol 2018; 15: 497–498.
    OpenUrl
  23. 23.
    1. Erickson BJ,
    2. Korfiatis P,
    3. Akkus Z,
    4. Kline T,
    5. Philbrick K.
    Toolkits and libraries for deep learning. J Digit Imaging 2017; 30: 400–405.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Kohli M,
    2. Prevedello LM,
    3. Filice RW,
    4. Geis JR.
    Implementing machine learning in radiology practice and research. AJR Am J Roentgenol 2017; 208: 754–760.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Qiu W,
    2. Kuang H,
    3. Teleg E,
    4. Ospel JM,
    5. Sohn SI,
    6. Almekhlafi M, et al.
    Machine learning for detecting early infarction in acute stroke with non-contrast-enhanced CT. Radiology 2020; 294: 638–644.
    OpenUrl
  26. 26.↵
    1. Yu KH,
    2. Beam AL,
    3. Kohane IS.
    Artificial intelligence in healthcare. Nat Biomed Eng 2018; 2: 719–731.
    OpenUrl
  27. 27.↵
    1. Tang A,
    2. Tam R,
    3. Cadrin-Chênevert A,
    4. Guest W,
    5. Chong J,
    6. Barfett J, et al.
    Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018; 69: 120–135.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. Guo J,
    2. Li B.
    The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2018; 2: 174–181.
    OpenUrl
  29. 29.
    1. Lakhani P,
    2. Sundaram B.
    Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017; 284: 574–582.
    OpenUrlCrossRefPubMed
  30. 30.
    American Medical Association. 3 ways medical AI can improve workflow for physicians. [Updated 2018; 2020 Oct 25]. Available from: https://www.ama-assn.org/practice-management/digital/3-ways-medical-ai-can-improve-workflow-physicians
  31. 31.
    1. Wang Z,
    2. Majewicz Fey A.
    Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int J Comput Assist Radiol Surg 2018; 13: 1959–1970.
    OpenUrl
  32. 32.↵
    1. Weng SF,
    2. Vaz L,
    3. Qureshi N,
    4. Kai J.
    Prediction of premature all-cause mortality: a prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS One 2019; 14: e0214365.
    OpenUrlCrossRef
  33. 33.↵
    1. Krittanawong C.
    The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med 2018; 48: e13–e14.
    OpenUrlPubMed
  34. 34.↵
    Food and Drug Administration. Considerations for the practical impact of AI in healthcare. [Updated 2021; 2020 Oct 25]. Available from: https://www.fda.gov/media/107792/download
  35. 35.↵
    1. Rock Health
    . How should the FDA approach the regulation of AI and machine learning in healthcare? [Updated 2018; 2020 Oct 25] Available from: https://rockhealth.com/how-should-the-fda-approach-the-regulation-of-ai-and-machine-learning-in-healthcare/
PreviousNext
Back to top

In this issue

Saudi Medical Journal: 43 (1)
Saudi Medical Journal
Vol. 43, Issue 1
1 Jan 2022
  • Table of Contents
  • Cover (PDF)
  • Index by author
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on Saudi Medical Journal.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Artificial intelligence in radiology
(Your Name) has sent you a message from Saudi Medical Journal
(Your Name) thought you would like to see the Saudi Medical Journal web site.
Citation Tools
Artificial intelligence in radiology
Mawya A. Khafaji, Mohammed A. Safhi, Roia H. Albadawi, Salma O. Al-Amoudi, Salah S. Shehata, Fadi Toonsi
Saudi Medical Journal Jan 2022, 43 (1) 53-60; DOI: 10.15537/smj.2022.43.1.20210337

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Artificial intelligence in radiology
Mawya A. Khafaji, Mohammed A. Safhi, Roia H. Albadawi, Salma O. Al-Amoudi, Salah S. Shehata, Fadi Toonsi
Saudi Medical Journal Jan 2022, 43 (1) 53-60; DOI: 10.15537/smj.2022.43.1.20210337
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • Abstract
    • Methods
    • Results
    • Discussion
    • Acknowledgment
    • Footnotes
    • References
  • Figures & Data
  • eLetters
  • References
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Emotional responses and coping strategies of medical students during the COVID-19 pandemic
  • Childhood nephrolithiasis and nephrocalcinosis caused by metabolic diseases and renal tubulopathy
  • The antimicrobial activity of ceftobiprole against Methicillin-resistant Staphylococcus aureus and multi-drug resistant Pseudomonas aeruginosa
Show more Original Articles

Similar Articles

Keywords

  • artificial intelligence
  • radiology
  • medical imaging

CONTENT

  • home

JOURNAL

  • home

AUTHORS

  • home
Saudi Medical Journal

© 2025 Saudi Medical Journal Saudi Medical Journal is copyright under the Berne Convention and the International Copyright Convention.  Saudi Medical Journal is an Open Access journal and articles published are distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC). Readers may copy, distribute, and display the work for non-commercial purposes with the proper citation of the original work. Electronic ISSN 1658-3175. Print ISSN 0379-5284.

Powered by HighWire