Abstract

Background: The US organ transplantation system is pursuing modernization of the allocation process through the integration of new technologies such as artificial intelligence (AI). However, the legal and ethical issues within the transplantation industry are still of concern. Objective: We explore the opportunities and challenges for Organ Procurement Organizations (OPOs) to adopt AI. The US organ transplant system is a highly regulated industry yet open to innovation. Methods: Ten structured interviews were conducted with OPO representatives using the Extended Technology, Organization, Environment (TOE) framework. Results: Overall, we identified five core tensions in AI adoption: (1) misconceptions, (2) approach to training, (3) need for AI expertise, (4) impact of organization size, and (5) top-down versus bottom-up adoption viewpoints. First, some of the positive perceptions of AI, such as bias elimination, are related to misconceptions about what is technically possible. Second, some OPOs believed that using AI systems requires basic knowledge about the AI system, while others stated that AI should be intuitive and require no training. Third, they disagreed on whether it is necessary to add AI-experienced staff as part of an AI adoption strategy. Fourth, smaller OPOs may struggle to develop, maintain, and implement AI systems due to their limited resources, yet they are more nimble and able to pivot due to less bureaucracy. Fifth, there are competing visions for how AI should be adopted across OPOs nationwide, either top-down driven by regulatory requirements or bottom-up driven by performance expectations. Conclusions: Ongoing work is needed to determine best practices for integrating AI in OPOs to support optimal organ use and expand transplant access for patients. The TOE framework highlights organization-level tensions that need to be addressed by the transplant sector for successful AI adoption and integration.

Department(s)

Psychological Science

Second Department

Engineering Management and Systems Engineering

Publication Status

Open Access

Comments

Missouri University of Science and Technology, Grant 2222801

Keywords and Phrases

AI adoption; Artificial intelligence; healthcare; technology–organization–environment framework; transplant

International Standard Serial Number (ISSN)

2055-2076

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 SAGE Publications, All rights reserved.

Publication Date

01 Jan 2026

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