Skip to main content

Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists

Medical Oncology volume 37, Article number: 40 (2020) Cite this article

Abstract

Artificial intelligence (AI) is revolutionizing healthcare and transforming the clinical practice of physicians across the world. Radiology has a strong affinity for machine learning and is at the forefront of the paradigm shift, as machines compete with humans for cognitive abilities. AI is a computer science simulation of the human mind that utilizes algorithms based on collective human knowledge and the best available evidence to process various forms of inputs and deliver desired outcomes, such as clinical diagnoses and optimal treatment options. Despite the overwhelmingly positive uptake of the technology, warnings have been published about the potential dangers of AI. Concerns have been expressed reflecting opinions that future medicine based on AI will render radiologists irrelevant. Thus, how much of this is based on reality? To answer these questions, it is important to examine the facts, clarify where AI really stands and why many of these speculations are untrue. We aim to debunk the 6 top myths regarding AI in the future of radiologists.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

References

  1. Pesapane F. How scientific mobility can help current and future radiology research: a radiology trainee's perspective. Insights Imaging. 2019;10(1):85. https://doi.org/10.1186/s13244-019-0773-z.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, et al. A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology. 2019;291(3):781–91. https://doi.org/10.1148/radiol.2019190613.

    Article  PubMed  Google Scholar 

  3. Ranschaert ER, Sergey M, Algra PR. Artificial intelligence in medical imaging. Berlin: Springer; 2019.

    Book  Google Scholar 

  4. Russell S, Bohannon J. Artificial intelligence. Fears of an AI pioneer. Science. 2015;349(6245):252. https://doi.org/10.1126/science.349.6245.252.

    Article  PubMed  Google Scholar 

  5. Pizzini FB, Pesapane F, Niessen W, Geerts-Ossevoort L, Broeckx N. ESMRMB Round table report on "Can Europe Lead in Machine Learning of MRI-Data?". MAGMA. 2020. https://doi.org/10.1007/s10334-019-00821-8.

    Article  PubMed  Google Scholar 

  6. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35. https://doi.org/10.1186/s41747-018-0061-6.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:13087. https://doi.org/10.1038/srep13087.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. Shaban-Nejad A, Michalowski M, Buckeridge D. Health intelligence: how artificial intelligence transforms population and personalized health. NPJ Digit Med. 2018;1(53):1–10.

    Google Scholar 

  10. Turing A. On computable numbers, with an application to the entsheidungsproblem. Proceedings of the London Mathematical Society, London, 1936; pp. 230–65.

  11. Judea P. Probabilistic reasoning in intelligent systems: networks of plausible inference. Commun ACM. 1988;62(3):54–60.

    Google Scholar 

  12. Tacher V, de Baere T. Robotic assistance in interventional radiology: dream or reality? Eur Radiol. 2020;30(2):925–6. https://doi.org/10.1007/s00330-019-06541-w.

    Article  PubMed  Google Scholar 

  13. Chockley K, Emanuel E. The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol. 2016;13(12(Pt A)):1415–20. https://doi.org/10.1016/j.jacr.2016.07.010.

    Article  PubMed  Google Scholar 

  14. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–9. https://doi.org/10.1056/NEJMp1606181.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2018;48:e13–e1414. https://doi.org/10.1016/j.ejim.2017.06.017.

    CAS  Article  PubMed  Google Scholar 

  16. Sardanelli F, Hunink MG, Gilbert FJ, Di Leo G, Krestin GP. Evidence-based radiology: why and how? Eur Radiol. 2010;20(1):1–15.

    Article  Google Scholar 

  17. Dodd JD. Evidence-based practice in radiology: steps 3 and 4-appraise and apply diagnostic radiology literature. Radiology. 2007;242(2):342–54. https://doi.org/10.1148/radiol.2422051679.

    Article  PubMed  Google Scholar 

  18. Sardanelli F. Trends in radiology and experimental research. Eur Radiol Exp. 2017. https://doi.org/10.1186/s41747-017-0006-5.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017;18(4):570–84. https://doi.org/10.3348/kjr.2017.18.4.570.

    Article  PubMed  PubMed Central  Google Scholar 

  20. King BF Jr. Guest editorial: discovery and artificial intelligence. AJR Am J Roentgenol. 2017;209(6):1189–90. https://doi.org/10.2214/AJR.17.19178.

    Article  PubMed  Google Scholar 

  21. Azavedo E, Zackrisson S, Mejare I, Heibert AM. Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review. BMC Med Imaging. 2012;12:22. https://doi.org/10.1186/1471-2342-12-22.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Dheeba J, Albert Singh N, Tamil SS. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inform. 2014;49:45–52. https://doi.org/10.1016/j.jbi.2014.01.010.

    CAS  Article  PubMed  Google Scholar 

  23. Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 2017;208(4):754–60. https://doi.org/10.2214/AJR.16.17224.

    Article  PubMed  Google Scholar 

  24. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77. https://doi.org/10.1148/radiol.2015151169.

    Article  PubMed  Google Scholar 

  25. Armbruster DA, Overcash DR, Reyes J. Clinical Chemistry Laboratory Automation in the 21st Century—Amat Victoria curam (Victory loves careful preparation). Clin Biochem Rev. 2014;35(3):143–53.

    PubMed  PubMed Central  Google Scholar 

  26. Pesapane F, Volonte C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging. 2018;9(5):745–53. https://doi.org/10.1007/s13244-018-0645-y.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Francavilla ML, Arleo EK, Bluth EI, Straus CM, Reddy S, Recht MP. Surveying academic radiology department chairs regarding new and effective strategies for medical student recruitment. AJR Am J Roentgenol. 2016;207(6):1171–5. https://doi.org/10.2214/AJR.16.16445.

    Article  PubMed  Google Scholar 

  28. Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA. 2016;316(22):2353–4. https://doi.org/10.1001/jama.2016.17438.

    Article  PubMed  Google Scholar 

  29. Ho CWL, Soon D, Caals K, Kapur J. Governance of automated image analysis and artificial intelligence analytics in healthcare. Clin Radiol. 2019;74(5):329–37. https://doi.org/10.1016/j.crad.2019.02.005.

    CAS  Article  PubMed  Google Scholar 

  30. Perez JK. (2018) V. Gartner’s hype cycle: a simple explanation. Int J Comput Optim. 2018;5(1):1–4. https://doi.org/10.12988/ijco.2018.832.

    Article  Google Scholar 

  31. Iannessi A, Marcy PY, Clatz O, Bertrand AS, Sugimoto M. A review of existing and potential computer user interfaces for modern radiology. Insights Imaging. 2018;9(4):599–609. https://doi.org/10.1007/s13244-018-0620-7.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Sapkaroski D, Mundy M, Dimmock MR. Virtual reality versus conventional clinical role-play for radiographic positioning training: a students' perception study. Radiography (London). 2020;26(1):57–62. https://doi.org/10.1016/j.radi.2019.08.001.

    CAS  Article  Google Scholar 

  33. Rhodes D. Why virtual/augmented reality hasn’t taken off yet. 2018. https://bdtechtalks.com/2018/09/05/virtual-reality-augmented-reality-hasnt-taken-off-yet/. Accessed 15 Jan 2020.

  34. Dankelman J, Wentink M, Grimbergen CA, Stassen HG, Reekers J. Does virtual reality training make sense in interventional radiology? Training skill-, rule- and knowledge-based behavior. Cardiovasc Intervent Radiol. 2004;27(5):417–21. https://doi.org/10.1007/s00270-004-0250-y.

    CAS  Article  PubMed  Google Scholar 

  35. Douglas DB, Boone JM, Petricoin E, Liotta L, Wilson E. Augmented reality imaging system: 3D viewing of a breast cancer. J Nat Sci. 2016;2(9):e215.

    PubMed  PubMed Central  Google Scholar 

  36. Kuhlemann I, Kleemann M, Jauer P, Schweikard A, Ernst F. Towards X-ray free endovascular interventions—using HoloLens for on-line holographic visualisation. Healthc Technol Lett. 2017;4(5):184–7. https://doi.org/10.1049/htl.2017.0061.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Erdal BS, Prevedello LM, Qian S, Demirer M, Little K, Ryu J, et al. Radiology and enterprise medical imaging extensions (REMIX). J Digit Imaging. 2018;31(1):91–106. https://doi.org/10.1007/s10278-017-0010-6.

    Article  PubMed  Google Scholar 

  38. Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, et al. Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol. 2018;15(2):350–9. https://doi.org/10.1016/j.jacr.2017.09.044.

    Article  PubMed  Google Scholar 

  39. Amisha MP, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328–31. https://doi.org/10.4103/jfmpc.jfmpc_440_19.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Riga CV, Bicknell CD, Wallace D, Hamady M, Cheshire N. Robot-assisted antegrade in-situ fenestrated stent grafting. Cardiovasc Intervent Radiol. 2009;32(3):522–4. https://doi.org/10.1007/s00270-008-9459-5.

    Article  PubMed  Google Scholar 

  41. Makris GC, Uberoi R. Interventional radiology-the future: evolution or extinction? Cardiovasc Intervent Radiol. 2016;39(12):1789–90. https://doi.org/10.1007/s00270-016-1450-y.

    Article  PubMed  Google Scholar 

  42. Kwan SW, Talenfeld AD, Brunner MC. The top three health care developments impacting the practice of interventional radiology in the next decade. AJR Am J Roentgenol. 2016. https://doi.org/10.2214/AJR.16.16435.

    Article  PubMed  Google Scholar 

  43. Kwan SW, Fidelman N, Ma E, Kerlan RK Jr, Yao FY. Imaging predictors of the response to transarterial chemoembolization in patients with hepatocellular carcinoma: a radiological-pathological correlation. Liver Transpl. 2012;18(6):727–36. https://doi.org/10.1002/lt.23413.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Verghese A, Shah NH, Harrington RA. What this computer needs is a physician: humanism and artificial intelligence. JAMA. 2018;319(1):19–20. https://doi.org/10.1001/jama.2017.19198.

    Article  PubMed  Google Scholar 

  45. Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. 2018;131(2):129–33. https://doi.org/10.1016/j.amjmed.2017.10.035.

    Article  PubMed  Google Scholar 

  46. Jha S. Will computers replace radiologists? In: Medscape. 2016. https://www.medscape.com/viewarticle/863127#7.

  47. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.

    CAS  Article  PubMed  Google Scholar 

  48. Wu H, Chan NK, Zhang CJP, Ming WK. The role of the sharing economy and artificial intelligence in health care: opportunities and challenges. J Med Internet Res. 2019;21(10):e13469. https://doi.org/10.2196/13469.

    Article  PubMed  PubMed Central  Google Scholar 

  49. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6. https://doi.org/10.1038/s41591-018-0307-0.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21(1):4–21. https://doi.org/10.1109/JBHI.2016.2636665.

    Article  PubMed  Google Scholar 

  51. Hessler M. The Triumph of "Stupidity": Deep Blue’s Victory over Garri Kasparov. The controversy about its impact on artificial intelligence research. NTM. 2017;25(1):1–33. https://doi.org/10.1007/s00048-017-0167-6.

    Article  PubMed  Google Scholar 

  52. Hansen A, Herrmann M, Ehlers JP, Mondritzki T, Hensel KO, Truebel H, et al. Perception of the progressing digitization and transformation of the German health care system among experts and the public: mixed methods study. JMIR Public Health Surveill. 2019;5(4):e14689. https://doi.org/10.2196/14689.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393(10181):1577–9. https://doi.org/10.1016/S0140-6736(19)30037-6.

    Article  PubMed  Google Scholar 

  54. Jaremko JL, Azar M, Bromwich R, Lum A, Alicia Cheong LH, Gibert M, et al. Canadian association of radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J. 2019;70(2):107–18. https://doi.org/10.1016/j.carj.2019.03.001.

    Article  PubMed  Google Scholar 

  55. Pesapane F, Standaert C, De Visschere P, Villeirs G. T-staging of prostate cancer: identification of useful signs to standardize detection of posterolateral extraprostatic extension on prostate MRI. Clin Imaging. 2020;59(1):1–7. https://doi.org/10.1016/j.clinimag.2019.08.007.

    Article  PubMed  Google Scholar 

  56. Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. 2018;6(11):837–45. https://doi.org/10.1016/S2213-2600(18)30286-8.

    Article  PubMed  Google Scholar 

  57. Padhani AR, Turkbey B. Detecting prostate cancer with deep learning for MRI: a small step forward. Radiology. 2019;293(3):618–9. https://doi.org/10.1148/radiol.2019192012.

    Article  PubMed  Google Scholar 

  58. Liu J, Sun D, Chen L, Fang Z, Song W, Guo D, et al. Radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging for the prediction of sentinel lymph node metastasis in breast cancer. Front Oncol. 2019;9:980. https://doi.org/10.3389/fonc.2019.00980.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Cingolani R. L’ altra specie. Otto domande su noi e loro. Intersezioni. Bologna, Italy: Il Mulino; 2019, p. 130–148

    Google Scholar 

  60. King BF Jr. Artificial intelligence and radiology: what will the future hold? J Am Coll Radiol. 2018;15((3 Pt B)):501–3. https://doi.org/10.1016/j.jacr.2017.11.017.

    Article  PubMed  Google Scholar 

  61. European Commission. Science, research and innovation performance of the EU 2018. 2019.

  62. Molteni M. Wellness apps evade the FDA, only to land in court. WIRED. 2017. https://www.wired.com/2017/04/wellness-apps-evade-fda-land-court/.

  63. Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15((3Pt B)):504–8. https://doi.org/10.1016/j.jacr.2017.12.026.

    Article  PubMed  Google Scholar 

  64. Yi PH, Hui FK, Ting DSW. Artificial intelligence and radiology: collaboration is key. J Am Coll Radiol. 2018. https://doi.org/10.1016/j.jacr.2017.12.037.

    Article  PubMed  Google Scholar 

  65. Calo R. Artificial Intelligence policy: a primer and roadmap. Social Science Research Network. 2017. https://lawreview.law.ucdavis.edu/issues/51/2/Symposium/51-2_Calo.pdf.

  66. Pesapane F, Suter MB, Codari M, Patella F, Volonté C, Sardanelli F. Regulatory issues for artificial intelligence in radiology. In: Precision Medicine for Investigators, Practitioners and Providers. 2020. https://doi.org/10.1016/B978-0-12-819178-1.00052-6.

  67. Thierer AD, O'Sullivan A, Russel R. Artificial intelligence and public policy. Mercatus Research Paper. 2017. https://www.mercatus.org/system/files/thierer-artificial-intelligence-policy-mr-mercatus-v1.pdf.

  68. Scherer MU. Regulating artificial intelligence systems: risks, challenges, competencies, and strategies. Harv JL & Tech. 2016;29(2):354–400.

    Google Scholar 

  69. Mitchell T, Brynjolfsson E. Track how technology is transforming work. Nature. 2017;544(7650):290–2. https://doi.org/10.1038/544290a.

    CAS  Article  PubMed  Google Scholar 

  70. Kramer DB, Xu S, Kesselheim AS. Regulation of medical devices in the United States and European Union. N Engl J Med. 2012;366(9):848–55. https://doi.org/10.1056/NEJMhle1113918.

    CAS  Article  PubMed  Google Scholar 

  71. European Economic Community. 93/42/EEC—Council Directive concerning Medical Devices. In: 12.7.93. Official Journal of the European Communities. 1993. https://ec.europa.eu/growth/single-market/european-standards/harmonised-standards/medical-devices_en.

  72. European Economic Community. 90/385/EEC—Council Directive on the approximation of the laws of the Member States relating to active implantable medical devices. Council Directive. 1990. https://ec.europa.eu/growth/single-market/european-standards/harmonised-standards/implantable-medical-devices_en.

  73. European Commission. Directive 98/79/EC of the European Parliament and of the Council on in vitro diagnostic medical devices. Official Journal of the European Communities. 1998. https://ec.europa.eu/growth/single-market/european-standards/harmonised-standards/iv-diagnostic-medical-devices_en.

  74. The European Parliament and the Council of The European Union. Regulation (EU) 2017/745 of the European Parliament and of the Council on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC. Official Journal of the European Communities. 2017. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32017R0745.

  75. The European Parliament and the Council of The European Union. Regulation (EU) 2017/746 of the European Parliament and of the Council on in vitro diagnostic medical devices and repealing Directive 98/79/EC and Commission Decision 2010/227/EU. Official Journal of the European Communities. 2017. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32017R0746.

  76. EL Crossley S. EU regulation of health information technology, software and mobile apps. Practical Law Global Guide. 2016;17(1):1–14.

    Google Scholar 

  77. 114th Congress (2015–2016). H.R.34–21st Century Cures Act. 2016. https://www.congress.gov/bill/114th-congress/house-bill/34.

  78. U.S. Food & Drug Administration. Is the product a medical device? U.S. Department of Health and Human Services. 2018. https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/Overview/ClassifyYourDevice/ucm051512.htm.

  79. Mendez AJ, Tahoces PG, Lado MJ, Souto M, Vidal JJ. Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms. Med Phys. 1998;25(6):957–64. https://doi.org/10.1118/1.598274.

    CAS  Article  PubMed  Google Scholar 

  80. Recht M, Bryan RN. Artificial intelligence: threat or boon to radiologists? J Am Coll Radiol. 2017;14(11):1476–80. https://doi.org/10.1016/j.jacr.2017.07.007.

    Article  PubMed  Google Scholar 

  81. Liew C. The future of radiology augmented with artificial intelligence: a strategy for success. Eur J Radiol. 2018;102:152–6. https://doi.org/10.1016/j.ejrad.2018.03.019.

    Article  PubMed  Google Scholar 

Download references

Author information

Affiliations

  1. Postgraduation School in Radiodiagnostics, Università Degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy

    Filippo Pesapane, Pierpaolo Biondetti & Luca Nicosia

  2. Interventional Radiology, King’s College Hospital, London, UK

    Priyan Tantrige

  3. Diagnostic and Interventional Radiology Department, San Paolo Hospital, University of Milan, Milan, Italy

    Francesca Patella

  4. UOC Radiologia, ASST Rhodense, Garbagnate Milanese, Italy

    Andrea Ianniello

  5. Department of Diagnostic Imaging, Interventional Radiology Unit, E.O. Galliera Hospital, Genova, Italy

    Umberto G. Rossi

  6. Radiology Department, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy

    Gianpaolo Carrafiello & Anna Maria Ierardi

  7. Dipartimento Di Scienze Della Salute, Università Degli Studi di Milano, Milano, Italy

    Gianpaolo Carrafiello

Authors
  1. Filippo Pesapane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Priyan Tantrige
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesca Patella
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pierpaolo Biondetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luca Nicosia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrea Ianniello
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Umberto G. Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Gianpaolo Carrafiello
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Anna Maria Ierardi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

(1) Conception and design: Dr. FP, Dr. AMI. (2) Administrative support: Dr. GC, Dr. AMI. (3) Provision of study materials or patients: All Authors. (4) Collection and assembly of data: All Authors. (5) Data analysis and interpretation: All Authors. (6) Manuscript writing: All authors. (7) Final approval of manuscript: All authors.

Corresponding author

Correspondence to Filippo Pesapane.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pesapane, F., Tantrige, P., Patella, F. et al. Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists. Med Oncol 37, 40 (2020). https://doi.org/10.1007/s12032-020-01368-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12032-020-01368-8

Keywords

  • Interventional radiology
  • Artificial intelligence
  • Machine learning
  • Deep learning