ReviewXenotransplantation
Artificial Intelligence in Xenotransplantation: A Prioritized Roadmap for Early Clinical Translation, Opportunities and Challenges.
Review in Xenotransplantation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Xenotransplantation represents a potential solution to the persistent global organ shortage, yet its clinical application remains stalled by complex immunologic responses, coagulation dysregulation, species-specific biology, and infectious risks. Artificial intelligence (AI) could enhance safety, accelerate decision-making, and enable precision medicine initiatives within this rapidly evolving field. However, effective implementation of AI in xenotransplantation requires approaches specifically adapted to the biological and operational complexities of cross-species transplantation. Here, we present our suggestion of a prioritized roadmap for integrating AI into early clinical xenotransplantation, based on clinical need, data availability, technical readiness, feasibility of clinician-supervised implementation, and potential impact on graft assessment and safety monitoring. Priority domains include digital pathology and imaging, machine perfusion-based viability monitoring, multimodal and multi-omics detection of graft injury and rejection, and surveillance for potential xenozoonotic infections. One of the essential prerequisites to ensure the development of reliable AI in xenotransplantation is to develop standardized definitions of xenograft injury phenotypes and ground truth datasets, which in this emerging field are currently lacking. The limitations to the application of AI in xenotransplantation, which include the lack of clinical data, species-specific differences, and delays in annotations and regulations, can be addressed via data sharing, federated learning, fairness, and validation. By combining gene-edited donors and refined immunosuppression regimens with clinically supervised, auditable, and transplant-specific, AI-based support systems, xenotransplantation could be made safer and more reproducible in the clinical arena.
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Registered trials
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.