Literature DB >> 33937846

The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings.

Yasasvi Tadavarthi1, Brianna Vey1, Elizabeth Krupinski1, Adam Prater1, Judy Gichoya1, Nabile Safdar1, Hari Trivedi1.   

Abstract

PURPOSE: To provide an overview of important factors to consider when purchasing radiology artificial intelligence (AI) software and current software offerings by type, subspecialty, and modality.
MATERIALS AND METHODS: Important factors for consideration when purchasing AI software, including key decision makers, data ownership and privacy, cost structures, performance indicators, and potential return on investment are described. For the market overview, a list of radiology AI companies was aggregated from the Radiological Society of North America and the Society for Imaging Informatics in Medicine conferences (November 2016-June 2019), then narrowed to companies using deep learning for imaging analysis and diagnosis. Software created for image enhancement, reporting, or workflow management was excluded. Software was categorized by task (repetitive, quantitative, explorative, and diagnostic), modality, and subspecialty.
RESULTS: A total of 119 software offerings from 55 companies were identified. There were 46 algorithms that currently have Food and Drug Administration and/or Conformité Européenne approval (as of November 2019). Of the 119 offerings, distribution of software targets was 34 of 70 (49%), 21 of 70 (30%), 14 of 70 (20%), and one of 70 (1%) for diagnostic, quantitative, repetitive, and explorative tasks, respectively. A plurality of companies are focused on nodule detection at chest CT and two-dimensional mammography. There is very little activity in certain subspecialties, including pediatrics and nuclear medicine. A comprehensive table is available on the website hitilab.org/pages/ai-companies.
CONCLUSION: The radiology AI marketplace is rapidly maturing, with an increase in product offerings. Radiologists and practice administrators should educate themselves on current product offerings and important factors to consider before purchase and implementation.© RSNA, 2020See also the invited commentary by Sala and Ursprung in this issue. 2020 by the Radiological Society of North America, Inc.

Entities:  

Year:  2020        PMID: 33937846      PMCID: PMC8082344          DOI: 10.1148/ryai.2020200004

Source DB:  PubMed          Journal:  Radiol Artif Intell        ISSN: 2638-6100


  23 in total

1.  Aggregate cost of mammography screening in the United States: comparison of current practice and advocated guidelines.

Authors:  Cristina O'Donoghue; Martin Eklund; Elissa M Ozanne; Laura J Esserman
Journal:  Ann Intern Med       Date:  2014-02-04       Impact factor: 25.391

Review 2.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

3.  Radiology Workflow Disruptors: A Detailed Analysis.

Authors:  Andrew Schemmel; Matthew Lee; Taylor Hanley; B Dustin Pooler; Tabassum Kennedy; Aaron Field; Douglas Wiegmann; John-Paul J Yu
Journal:  J Am Coll Radiol       Date:  2016-06-14       Impact factor: 5.532

Review 4.  The future of radiology augmented with Artificial Intelligence: A strategy for success.

Authors:  Charlene Liew
Journal:  Eur J Radiol       Date:  2018-03-14       Impact factor: 3.528

5.  Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Authors:  Alejandro Rodríguez-Ruiz; Elizabeth Krupinski; Jan-Jurre Mordang; Kathy Schilling; Sylvia H Heywang-Köbrunner; Ioannis Sechopoulos; Ritse M Mann
Journal:  Radiology       Date:  2018-11-20       Impact factor: 11.105

Review 6.  Artificial intelligence in radiology.

Authors:  Ahmed Hosny; Chintan Parmar; John Quackenbush; Lawrence H Schwartz; Hugo J W L Aerts
Journal:  Nat Rev Cancer       Date:  2018-08       Impact factor: 60.716

Review 7.  Artificial intelligence for diabetic retinopathy screening: a review.

Authors:  Andrzej Grzybowski; Piotr Brona; Gilbert Lim; Paisan Ruamviboonsuk; Gavin S W Tan; Michael Abramoff; Daniel S W Ting
Journal:  Eye (Lond)       Date:  2019-09-05       Impact factor: 3.775

Review 8.  Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.

Authors:  Rajiv Raman; Sangeetha Srinivasan; Sunny Virmani; Sobha Sivaprasad; Chetan Rao; Ramachandran Rajalakshmi
Journal:  Eye (Lond)       Date:  2018-11-06       Impact factor: 3.775

9.  Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.

Authors:  Weicheng Kuo; Christian Hӓne; Pratik Mukherjee; Jitendra Malik; Esther L Yuh
Journal:  Proc Natl Acad Sci U S A       Date:  2019-10-21       Impact factor: 11.205

10.  Integrating AI into radiology workflow: levels of research, production, and feedback maturity.

Authors:  Engin Dikici; Matthew Bigelow; Luciano M Prevedello; Richard D White; Barbaros S Erdal
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-11
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  6 in total

1.  Forging Connections in Latin America to Advance AI in Radiology.

Authors:  Felipe Campos Kitamura; Felipe Barjud Pereira do Nascimento; Guillermo Elizondo-Riojas; Hernán Chaves; Héctor Henríquez Leighton; Emmanuel Salinas-Miranda; Thiago Júlio; Antônio José da Rocha; César Higa Nomura
Journal:  Radiol Artif Intell       Date:  2022-08-31

2.  Artificial intelligence for radiological paediatric fracture assessment: a systematic review.

Authors:  Susan C Shelmerdine; Richard D White; Hantao Liu; Owen J Arthurs; Neil J Sebire
Journal:  Insights Imaging       Date:  2022-06-03

3.  Integrating Al Algorithms into the Clinical Workflow.

Authors:  Krishna Juluru; Hao-Hsin Shih; Krishna Nand Keshava Murthy; Pierre Elnajjar; Amin El-Rowmeim; Christopher Roth; Brad Genereaux; Josef Fox; Eliot Siegel; Daniel L Rubin
Journal:  Radiol Artif Intell       Date:  2021-08-04

Review 4.  What Makes Artificial Intelligence Exceptional in Health Technology Assessment?

Authors:  Jean-Christophe Bélisle-Pipon; Vincent Couture; Marie-Christine Roy; Isabelle Ganache; Mireille Goetghebeur; I Glenn Cohen
Journal:  Front Artif Intell       Date:  2021-11-02

5.  Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically-a retrospective study.

Authors:  Ghala Alotaibi; Mohammed Awawdeh; Fathima Fazrina Farook; Mohamed Aljohani; Razan Mohamed Aldhafiri; Mohamed Aldhoayan
Journal:  BMC Oral Health       Date:  2022-09-13       Impact factor: 3.747

Review 6.  Deep learning in breast imaging.

Authors:  Arka Bhowmik; Sarah Eskreis-Winkler
Journal:  BJR Open       Date:  2022-05-13
  6 in total

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