Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 28, 2023
Open Peer Review Period: Oct 28, 2023 - Dec 23, 2023
Date Accepted: Apr 18, 2024
(closed for review but you can still tweet)
Artificial Intelligence as the Proverbial Panacea? – A Taxonomy and Archetypes of AI-Based Healthcare Services
ABSTRACT
Background:
To cope with the enormous burdens placed on healthcare systems around the world – from the strains and stresses caused by longer life expectancy to the large-scale emergency relief actions required by pandemics like COVID-19 – many healthcare companies have been using artificial intelligence to adapt their services. Nevertheless, conceptual insights into how AI has been transforming the healthcare sector are still few and far between.
Objective:
This study aims to provide an overarching structure with which to classify the various real-world phenomena. A clear and comprehensive taxonomy will provide consensus on AI-based healthcare service offerings and sharpen the view of their adoption in the healthcare sector. Thus, this study’s goal is to identify design characteristics of AI-based healthcare services.
Methods:
We propose a multi-layered taxonomy created in accordance with the method of taxonomy development by Nickerson et al. 2013 and the recent extension by Kundisch et al. 2021. In doing so, we applied 268 AI-based healthcare services, conducted a structured literature review and then conducted an evaluation of the resulting taxonomy. Last, we have performed a cluster analysis to identify archetypes of AI-based healthare services.
Results:
We identified four key perspectives – agents, data, AI, and health impact – whereupon a cluster analysis yielded 13 archetypes that demonstrate our taxonomy’s applicability.
Conclusions:
This contribution to conceptual knowledge on AI-based healthcare services enables researchers as well as practitioners to analyze such services and improve their theory-led design.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.