nach oben

Erschienen in:

01.05.2020 | Review Article

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

verfasst von: Filippo Pesapane, Priyan Tantrige, Francesca Patella, Pierpaolo Biondetti, Luca Nicosia, Andrea Ianniello, Umberto G. Rossi, Gianpaolo Carrafiello, Anna Maria Ierardi

Erschienen in: Medical Oncology | Ausgabe 5/2020

Einloggen, um Zugang zu erhalten

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.

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.

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.

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMed

18.

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

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.PubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.CrossRef

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.CrossRefPubMed

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.PubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

59.

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

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.

69.

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

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.CrossRefPubMed

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.

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

Titel: Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists
verfasst von: Filippo Pesapane
Priyan Tantrige
Francesca Patella
Pierpaolo Biondetti
Luca Nicosia
Andrea Ianniello
Umberto G. Rossi
Gianpaolo Carrafiello
Anna Maria Ierardi
Publikationsdatum: 01.05.2020
Verlag: Springer US
Erschienen in: Medical Oncology / Ausgabe 5/2020
Print ISSN: 1357-0560
Elektronische ISSN: 1559-131X
DOI: https://doi.org/10.1007/s12032-020-01368-8

Neu im Fachgebiet Onkologie

19.04.2024 | EAU 2024 | Kongressbericht | Nachrichten

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

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

Abstract

Neu im Fachgebiet Onkologie

Prostatakarzinom: EU initiiert neues Screeningkonzept

Blasenkarzinom – Biomarker statt Zytologie?

Stereotaktische Radiatio schlägt konventionelle Bestrahlung

Adjuvante Brustkrebstherapie: Doppelt hält besser

Update Onkologie

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 5/2020

Delta-radiomics increases multicentre reproducibility: a phantom study

Cellular apoptosis susceptibility protein (CAS) suppresses the proliferation of breast cancer cells by upregulated cyp24a1

Identification of novel biomarkers, MUC5AC, MUC1, KRT7, GAPDH, CD44 for gastric cancer

Comparative bioinformatical analysis of pancreatic head cancer and pancreatic body/tail cancer

Preliminary results in unresectable cholangiocarcinoma treated by CT percutaneous irreversible electroporation: feasibility, safety and efficacy

PD-L1 upregulation accompanied with epithelial–mesenchymal transition attenuates sensitivity to ATR inhibition in p53 mutant pancreatic cancer cells

Neu im Fachgebiet Onkologie

Prostatakarzinom: EU initiiert neues Screeningkonzept

Blasenkarzinom – Biomarker statt Zytologie?

Stereotaktische Radiatio schlägt konventionelle Bestrahlung

Adjuvante Brustkrebstherapie: Doppelt hält besser

Update Onkologie