Evidence map›Paper›PMID 41883552›Full record

ArticleDigital health

Five tensions of artificial intelligence adoption for organ allocation: Applying the technology-organization-environment framework.

Amaneh Babaee, Daniel B Shank, Casey Canfield, Joely Grace Hall, Krista L Lentine, Henry Randall, Mark Schnitzler

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Amaneh BabaeePsychology Department, Missouri University of Science and Technology, Rolla, MO, USA.ORCID https://orcid.org/0009-0005-8331-6135
Daniel B ShankPsychology Department, Missouri University of Science and Technology, Rolla, MO, USA.ORCID https://orcid.org/0000-0002-3746-2407
Casey CanfieldPsychology Department, Missouri University of Science and Technology, Rolla, MO, USA.ORCID https://orcid.org/0000-0001-5325-3798
Joely Grace HallPsychology Department, Missouri University of Science and Technology, Rolla, MO, USA.ORCID https://orcid.org/0009-0008-0033-4461
Krista L LentineSchool of Medicine, Saint Louis University, St Louis, MO, USA.ORCID https://orcid.org/0000-0002-9423-4849
Henry RandallSchool of Medicine, Saint Louis University, St Louis, MO, USA.ORCID https://orcid.org/0000-0003-1413-0386
Mark SchnitzlerSchool of Medicine, Saint Louis University, St Louis, MO, USA.ORCID https://orcid.org/0000-0001-8547-5643

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AI adoptionArtificial intelligencehealthcaretechnology–organization–environment frameworktransplant

Identifiers

PMID41883552
PMCPMC13009824

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.