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Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Jul 11, 2024)

Date Submitted: May 1, 2023

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Hybrid Business Models for AI and AR/VR Healthcare Solutions: Synergies, Complementarities, and Implementation Challenges

  • Ignat Kulkov; 
  • Julia Kulkova; 
  • Rene Rohrbeck

ABSTRACT

The rapid advancements in Artificial Intelligence (AI), Augmented Reality (AR), and Virtual Reality (VR) technologies present significant opportunities for the healthcare industry. This paper explores the development and optimization of hybrid business models that leverage the combined potential of AI, AR, and VR in healthcare. We examine the synergies and complementarities of these technologies, focusing on their ability to enhance diagnostics, personalized treatment, and medical training. Furthermore, we discuss implementation challenges, including technological, organizational, and cultural barriers, as well as data privacy, security, and regulatory compliance issues. Drawing from case studies of successful hybrid business models in healthcare, we analyze the factors contributing to their success and provide insights for other healthcare organizations and technology companies. Finally, we offer recommendations for creating and optimizing hybrid business models, addressing implementation challenges, and fostering collaboration among stakeholders. Our findings contribute to the literature on AI, AR, and VR in healthcare and provide practical implications for healthcare organizations and technology companies seeking to innovate in this rapidly evolving field.


 Citation

Please cite as:

Kulkov I, Kulkova J, Rohrbeck R

Hybrid Business Models for AI and AR/VR Healthcare Solutions: Synergies, Complementarities, and Implementation Challenges

JMIR Preprints. 01/05/2023:48619

DOI: 10.2196/preprints.48619

URL: https://preprints.jmir.org/preprint/48619

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