Skip to main content

Advertisement

SpringerLink
Log in

The first use of artificial intelligence (AI) in the ER: triage not diagnosis

  • Review Article
  • Published:
Emergency Radiology Aims and scope Submit manuscript

Abstract

Predictions related to the impact of AI on radiology as a profession run the gamut from AI putting radiologists out of business to having no effect at all. The use of AI appears to show significant promise in ER triage in the present. We briefly discuss the emerging effectiveness of AI in the ER imaging setting by looking at some of the products approved by the FDA and finding their way into “practice.” The FDA approval process to date has focused on applications that affect patient triage and not necessarily ones that have the computer serve as the only or final reader. We describe a select group of applications to provide the reader with a sense of the current state of AI use in the ER setting to assess neurologic, pulmonary, and musculoskeletal trauma indications. In the process, we highlight the benefits of triage staging using AI, such as accelerating diagnosis and optimizing workflow, with few downsides. The ability to triage patients and take care of acute processes such as intracranial bleed, pneumothorax, and pulmonary embolism will largely benefit the health system, improving patient care and reducing costs. These capabilities are all available now. This first wave of AI applications is not replacing radiologists. Rather, the innovative software is improving throughput, contributing to the timeliness in which radiologists can get to read abnormal scans, and possibly enhances radiologists’ accuracy. As for what the future holds for the use of AI in radiology, only time will tell.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Kulkarni S, Seneviratne N, Baig MS, Khan AHA (2020) Artificial intelligence in medicine: where are we now? Acad Radiol 27(1):62–70

    Article  Google Scholar 

  2. Jha S, Cook T (2020) Artificial intelligence in radiology—the state of the future. Acad Radiol 27(1):1–2

    Article  Google Scholar 

  3. Bryan RN, Davatzikos C, Herskovits EH, Mohan S, Rudie JD, Rauschecker AM (2020) Medical image analysis: human and machine. Acad Radiol 27(1):76–81

    Article  Google Scholar 

  4. Allen B, Dreyer K, McGinty GD (2019) Integrating artificial intelligence into radiologic practice: a look to the future. J Am Coll Radiol

  5. Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, Chepelev L, Cairns R, Mitchell JR, Cicero MD, Poudrette MG, Jaremko JL, Reinhold C, Gallix B, Gray B, Geis R, Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group (2018) Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 69(2):120–135

    Article  Google Scholar 

  6. Shi J, Sahiner B, Chan HP, Hadjiiski L, Zhou C, Cascade PN, Bogot N, Kazerooni EA, Wu YT, Wei J (2007) Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching. Med Phys 34(4):1336–1347

    Article  Google Scholar 

  7. Baker JA, Kornguth PJ, Lo JY, Williford ME, Floyd CE Jr (1995) Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology. 196(3):817–822

    Article  CAS  Google Scholar 

  8. Maccioni F Israeli brain scan-reading AI-based software gets thumbs up from FDA, Time The Times of Israel October 15, 2018. https://www.timesofisrael.com/israeli-brain-scan-reading-ai-based-software-gets-thumbs-up-from-fda-time/. Accessed Aug 19 2019

  9. Halon E. Zebra Medical wins FDA clearance for AI intracranial hemorrhage detection. The Jerusalem Post. June 17, 2019. https://www.jpost.com/HEALTH-SCIENCE/Zebra-Medical-wins-FDA-approval-for-AI-intracranial-hemorrhage-detection-592815. Accessed 19 Aug 2019

  10. MaxQ AI receives FDA clearance for Accipio Ix intracranial hemorrhage platform. Imaging Technology News. November 7, 2018. https://www.itnonline.com/content/maxq-ai-receives-fda-clearance-accipio-ix-intracranial-hemorrhage-platform. Accessed 1/29/20

  11. Brown D FDA clears AI detection software for diagnosing brain bleeds. AI in Healthcare. Nov. 8, 2018. https://www.aiin.healthcare/topics/diagnostics/fda-clears-ai-detection-software-brain-bleeds. Accessed 1/29/20

  12. Department of Health and Human Services, US Food and Drug Administration. Indications for use, BriefCase. August 8. 2018

  13. Aidoc receives FDA clearance for AI detection of acute intracranial hemorrhage. Imaging Technology News. August 8, 2018. https://www.itnonline.com/content/aidoc-receives-fda-clearance-ai-detection-acute-intracranial-hemorrhage. Accessed 2/3/20

  14. Ojeda P, Zawaideh M, Mossa-Basha M, Haynor D The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies. Proceedings Vol. 10949, Medical Imaging 2019: Image Processing, March 15, 2019, SPIE Medical Imaging, 2019, San Diego, CA, USA

  15. Third FDA clearance announced for Zebra-Med’s AI solution for brain bleed alerts. Imaging Technology News. June 19, 2019. https://www.itnonline.com/content/third-fda-clearance-announced-zebra-meds-ai-solution-brain-bleed-alerts. Accessed 2/3/20

  16. Ramos LA, van der Steen WE, Sales Barros R, Majoie CBLM, van den Berg R, Verbaan D, Vandertop WP, Zijlstra IJAJ, Zwinderman AH, Strijkers GJ, Olabarriaga SD, Marquering HA (2019) Machine learning improves prediction of delayed cerebral ischemia in patients subarachnoid hemorrhage. J Neurointerv Surg 11(5):497–502

    Article  Google Scholar 

  17. Zhang Y, Zhang B, Liang F, Liang S, Zhang Y, Yan P, Ma C, Liu A, Guo F, Jiang C (2019) Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types. Eur Radiol 29(4):2157–2165

    Article  Google Scholar 

  18. Chilamkurthy S, Ghosh R, Tanamala S et al (2018) Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 392(10162):2388–2396

    Article  Google Scholar 

  19. Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, Ebert SA, Pomerantz SR, Romero JM, Kamalian S, Gonzalez RG, Lev MH, Do S (2019) An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 3(3):173–182

    Article  Google Scholar 

  20. Weikert TJ, Winkel DJ, Bremerich J, et al. AI-powered detection of pulmonary embolism in CT pulmonary angiograms: a validation study of the diagnostic performance of prototype algorithms. ECR Online. Abstract: https://ecronline.myesr.org/ecr2019/index.php?p=recorddetail&rid=139db6ef-260e-4d96-a1b4-3808434b7495&t=browsesessions#ipp-record-8210bd69-763b-4799-8f35-03de381ad49a. Accessed 8/16/2019

  21. Taylor AG, Mielke C, Mongan J (2018 Nov 20) Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: a retrospective study. PLoS Med 15(11):e1002697

    Article  Google Scholar 

  22. Walter M AI-powered triage solution for pneumothorax gains FDA clearance. Radiology Business. May 13, 2019. https://www.radiologybusiness.com/topics/artificial-intelligence/ai-triage-pneumothorax-fda-clearance-zebra-medical. Accessed 19 Aug 2019

  23. Rosenfield S. FDA clears GE healthcare’s X-ray AI suite. Inside Digital Health, September, 12, 2019. https://www.idigitalhealth.com/news/fda-clears-ge-healthcares-xray-ai-suite. Accessed 1/9/2020

  24. Harvey HB, Gowda V (2020) How the FDA regulates AI. Acad Radiol 27(1):58–61

    Article  Google Scholar 

  25. Gan K, Xu D, Lin Y et al (2019) Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop 3:1–12

    Google Scholar 

  26. Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, Sköldenberg O, Gordon M (2017) Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop 88(6):581–586

    Article  Google Scholar 

  27. Lindsey R, Daluiski A, Chopra S et al (2018) Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A 115(45):11591–11596

    Article  CAS  Google Scholar 

  28. Kim DH, MacKinnon T (2018) Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol 73(5):439–445

    Article  CAS  Google Scholar 

  29. Yaeger KA, Martini M, Yaniv G, Oermann EK, Costa AB (2019) United States regulatory approval of medical devices and software applications enhanced by AI. Health Pol Tech 8(2):192–197

    Article  Google Scholar 

  30. Gottlieb S. Transforming FDA’s approach to digital health. US Food and Drug Administration. Speech, April 26, 2018. https://www.fda.gov/NewsEvents/Speeches/ucm605697.htm. Accessed 2/3/20

  31. US Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). Discussion Paper and Request for Feedback. https://www.fda.gov/media/122535/download. Accessed 2/3/20

  32. Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J (2018) Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 15(3 Pt B):504–508

    Article  Google Scholar 

  33. Mazurowski MA (2019) Artificial intelligence may cause a significant disruption to the radiology workforce. J Am Coll Radiol 16(8):1077–1082

    Article  Google Scholar 

  34. Allen B, Dreyer K, McGinty GB (2020) Integrating artificial intelligence into radiologic practice: a look to the future. J Am Coll Radiol 17(2):280–283

    Article  Google Scholar 

  35. Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, Nicolaou S (2019) Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study. Acad Radiol 26(4):566–577

    Article  Google Scholar 

  36. Mazurowski MA (2020) Artificial intelligence in radiology: some ethical considerations for radiologists and algorithm developers. Acad Radiol 27(1):127–129

    Article  Google Scholar 

  37. Lam G, Fishman EK, Horton KM, Rowe SP (2019) Simplifying complexity: lessons for radiology from a new type of stock exchange. J Am Coll Radiol 16(4 Pt A):536–538

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 601 North Caroline Street, JHOC 3262, Baltimore, MD, 21287, USA

    Edmund M. Weisberg

  2. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 600 North Wolfe Street, Hal B168, Baltimore, MD, 21287, USA

    Linda C. Chu

  3. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 601 North Caroline Street, JHOC 3254, Baltimore, MD, 21287, USA

    Elliot K. Fishman

Authors
  1. Edmund M. Weisberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linda C. Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elliot K. Fishman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edmund M. Weisberg.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

The authors declare that they had full access to all of the data in this study and the authors take complete responsibility for the integrity of the data and the accuracy of the data analysis.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weisberg, E.M., Chu, L.C. & Fishman, E.K. The first use of artificial intelligence (AI) in the ER: triage not diagnosis. Emerg Radiol 27, 361–366 (2020). https://doi.org/10.1007/s10140-020-01773-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10140-020-01773-6

Keywords

Search

Navigation