2023 Industry Perceptions Survey on AI Adoption and Return on Investment.

AI Adoption Artificial Intelligence Market Survey

Journal

Journal of imaging informatics in medicine
ISSN: 2948-2933
Titre abrégé: J Imaging Inform Med
Pays: Switzerland
ID NLM: 9918663679206676

Informations de publication

Date de publication:
20 Aug 2024
Historique:
received: 05 03 2024
accepted: 17 05 2024
revised: 15 05 2024
medline: 21 8 2024
pubmed: 21 8 2024
entrez: 20 8 2024
Statut: aheadofprint

Résumé

This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been progress in adopting AI across multiple uses, and there continues to be an optimistic forecast for the impact on workflow and clinical outcomes. This report, as in prior years, should be seen as a snapshot of the use of AI in imaging. Compared to our 2021 survey, the 2023 respondents expressed wider AI adoption but felt this was behind the potential. Specifically, the adoption has increased as sources of return on investment with AI in radiology are better understood as documented by vendor/client use case studies. Generally, the discussions of AI solutions centered on workflow triage, visualization, detection, and characterization. Generative AI was also mentioned for improving productivity in reporting. As payor reimbursement remains elusive, the ROI discussions expanded to look at other factors, including increased hospital procedures and admissions, enhanced radiologist productivity for practices, and improved patient outcomes for integrated health networks. When looking at the longer-term horizon for AI adoption, respondents frequently mentioned that the opportunity for AI to achieve greater adoption with more complex AI and a more manageable/visible ROI is outside the USA. Respondents focused on the barriers to trust in AI and the FDA processes.

Identifiants

pubmed: 39164452
doi: 10.1007/s10278-024-01147-1
pii: 10.1007/s10278-024-01147-1
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s).

Auteurs

Mitchell Goldburgh (M)

NTT DATA, Tokyo, Japan. Mitchell.Goldburgh@nttdata.com.

Michael LaChance (M)

LaChance Executive Consulting, Washington, DC, USA.

Julia Komissarchik (J)

Glendor, Inc., Draper, UT, USA.

Julia Patriarche (J)

A.I. Analysis, Inc., Seattle, WA, USA.

Oliver Chen (O)

HOPPR, Chicago, IL, USA.

Priya Deshpande (P)

Department of Electrical and Computer Engineering, Opus College of Engineering, Marquette University, Milwaukee, WI, USA.

Matthew Geeslin (M)

Deparment of Radiology, University of Vermont, Burlington, VT, USA.

Julia Komissarchik (J)

Glendor, Inc., Draper, UT, USA.

Nina Kottler (N)

Radiology Partners, El Segundo, CA, USA.

Julia Patriarche (J)

A.I. Analysis, Inc., Seattle, WA, USA.

Jennifer Sommer (J)

Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.

Marcus Ayers (M)

NTT DATA, Tokyo, Japan.

Vedrana Vujic (V)

NTT DATA, Tokyo, Japan.

Classifications MeSH