Artificial intelligence in preclinical nonhuman primate xenotransplantation: bridging the gap from data complexity to clinical precision

The review positions nonhuman primate preclinical models as an indispensable gateway for translating xenotransplantation into human clinical trials, and notes that these models generate high-dimensional immunological, physiological, behavioral, and histopathological datasets that are difficult to interpret with conventional analytical approaches, thereby limiting the precision of translational inference. Relative to prior literature that discusses xenotransplantation or artificial intelligence in isolation, this work explicitly identifies data complexity as a translational bottleneck and positions AI as an integrative response to it. This is a synthetic review-level judgment based on the authors' survey of existing evidence; the text provides no specific dataset sizes or quantitative metrics.

The review systematically enumerates five directions of AI application: computer vision for continuous noninvasive behavioral phenotyping and pain assessment; anomaly detection algorithms for extracting early warning signals from biosignal streams; multiomics integration for identifying xenograft-specific biomarker signatures; digital twin frameworks for hypothesis-generating simulations of prospective human recipient responses; and explainable AI for making model outputs more transparent and regulatory defensible. It organizes these directions into a coherent framework spanning behavioral, physiological, molecular, and simulation levels rather than presenting an isolated list of techniques. This is a domain review of existing evidence; the degree of validation for each direction is not quantified individually in the text.

The review proposes a research agenda aimed at making AI-augmented nonhuman primate experimentation a bridge from preclinical data complexity to clinical precision in xenotransplantation, and notes that the framework also supports the 3Rs (Replacement, Reduction, and Refinement) ethical mandate, aligning scientific rigor with animal welfare imperatives. It explicitly couples the technical roadmap with animal welfare ethics, turning the 3Rs from an external constraint into an integral part of the framework. This is an agenda-setting proposal and ethical argument whose feasibility awaits testing in subsequent research.

This article is positioned as a review and research agenda, intended for researchers, methodologists, and regulatory-oriented readers interested in preclinical translation of xenotransplantation, data analysis of nonhuman primate experiments, and the application of artificial intelligence in biomedical research. The proposed framework addresses the specific setting of nonhuman primate preclinical work, discussing how AI methods can handle high-dimensional data in that setting, improve the precision of translational inference, and simultaneously serve 3R ethical requirements.

The article presents a framework and agenda in review form; the actual degree of validation, data availability, and regulatory acceptability of each AI direction in nonhuman primate xenotransplantation remain to be clarified through subsequent research. The digital twin framework is described as potentially enabling hypothesis-generating simulations, and its predictive capability awaits testing. In addition, the reading scope here is abstract-level information; the specific content of the methodological limitations mentioned, the details of the original studies underlying each direction, and the concrete steps of the research agenda are not elaborated in the visible text, and these would affect further judgment of evidence strength.