Literature DB >> 32643069

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

Edmund M Weisberg1, Linda C Chu2, Elliot K Fishman3.   

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.

Entities:  

Keywords:  Artificial intelligence (AI); Intracranial bleed; Pneumothorax; Pulmonary embolism; Wrist fracture

Mesh:

Year:  2020        PMID: 32643069     DOI: 10.1007/s10140-020-01773-6

Source DB:  PubMed          Journal:  Emerg Radiol        ISSN: 1070-3004


  6 in total

Review 1.  Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective.

Authors:  Cheryl Beegle; Navid Hasani; Roberto Maass-Moreno; Babak Saboury; Eliot Siegel
Journal:  PET Clin       Date:  2022-01

2.  The Cases for and against Artificial Intelligence in the Medical School Curriculum.

Authors:  Brandon Ngo; Diep Nguyen; Eric vanSonnenberg
Journal:  Radiol Artif Intell       Date:  2022-08-17

3.  Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.

Authors:  Albert Boonstra; Mente Laven
Journal:  BMC Health Serv Res       Date:  2022-05-18       Impact factor: 2.908

Review 4.  Update on establishing and managing an overnight emergency radiology division.

Authors:  Meir H Scheinfeld; R Joshua Dym
Journal:  Emerg Radiol       Date:  2021-04-21

Review 5.  Machine Learning and Precision Medicine in Emergency Medicine: The Basics.

Authors:  Sangil Lee; Samuel H Lam; Thiago Augusto Hernandes Rocha; Ross J Fleischman; Catherine A Staton; Richard Taylor; Alexander T Limkakeng
Journal:  Cureus       Date:  2021-09-01

6.  Automated detection of pulmonary embolism from CT-angiograms using deep learning.

Authors:  Heidi Huhtanen; Mikko Nyman; Tarek Mohsen; Arho Virkki; Antti Karlsson; Jussi Hirvonen
Journal:  BMC Med Imaging       Date:  2022-03-14       Impact factor: 1.930

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.