Aug 2026· Xenotransplantation· Vol 33· 0 citations· 58 references
Medicine
TL;DR
A prioritized roadmap for integrating AI into early clinical xenotransplantation is presented, based on clinical need, data availability, technical readiness, feasibility of clinician‐supervised implementation, and potential impact on graft assessment and safety monitoring.
Abstract
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.
The purpose of this review is to summarize the most influential and conceptually significant publications from the past 2 years, including substantial 2026 publications, and to identify emerging directions likely to shape xenotransplantation and regenerative medicine in the near future. Advances in artificial intelligence (AI) now support more structured anticipation of future developments by integrating patterns across experimental, computational, and translational research. The field is approaching a potential inflection point in which increasingly capable AI systems, potentially approaching artificial general intelligence, may accelerate the design of stem-cell-derived tissues and progressively more complex organ constructs. In addition, scientific communication is evolving toward formats that support machine-assisted analysis and AI-driven knowledge synthesis. Multiple developments signal significant expansion across xenotransplantation and regenerative medicine, driven by innovations in gene editing, multimodal data integration, and AI-enabled prediction and decision-support systems. These advances will help to broaden access to transplantable organs and increase the scale and impact of the field across clinical practice, research, and workforce domains. Together, these trends suggest that AI-enabled regenerative and xenogeneic strategies may meaningfully reduce the organ shortage and support future progress toward precision-engineered organ replacement.
K. Solez, Habba F. Mahal, Wisit Cheungpasitporn et al.· Renal Failure· 0 citations
INTRODUCTION
Artificial intelligence (AI) is increasingly recognized as a transformative paradigm within transplantation medicine, offering advanced computational approaches capable of integrating heterogeneous clinical, biological, imaging, and molecular datasets to improve predictive accuracy and decision-making. Liver transplantation represents a uniquely complex clinical domain characterized by high-dimensional data, nonlinear interactions among risk factors, and critical time-dependent decision processes, thereby providing an ideal context for AI-enabled analytics.
METHODS
The objective of this systematic review was to critically synthesize current evidence regarding AI applications in liver transplantation, with emphasis on data modalities, algorithmic methodologies, targeted clinical outcomes, validation strategies, and reported performance metrics. A comprehensive search of MEDLINE, Scopus, and the Cochrane Library identified 1045 records following duplicate removal and automated filtering.
RESULTS
After screening and eligibility assessment, 65 studies met the inclusion criteria. Laboratory data represented the most frequently utilized input (n = 35), followed by clinical (n = 28), demographic (n = 19), imaging (n = 13), and genetic or molecular data (n = 5), with several studies employing multimodal integration. Deep-learning architectures and neural network-based approaches predominated, with additional contributions from ensemble learning methods and conventional machine-learning algorithms. Across multiple clinical domains-including diagnostic classification, prognostic modeling, graft survival prediction, and treatment optimization-AI systems demonstrated high predictive performance, frequently surpassing traditional risk stratification tools such as model for end-stage liver disease and Survival Outcomes Following Liver Transplantation scores. Imaging-based models achieved particularly strong segmentation accuracy, whereas genomic and molecular approaches demonstrated excellent discriminative capability in oncologic and graft-related outcomes.
CONCLUSIONS
Despite these promising findings, significant methodological limitations persist, including data heterogeneity, insufficient external validation, risk of bias, and challenges related to interpretability, fairness, and ethical deployment. Overall, AI represents a highly promising adjunct to clinical decision-making in liver transplantation; however, robust prospective validation, standardized reporting frameworks, and clinically interpretable implementations remain necessary prior to widespread adoption.
Panagiotis Boutos, James L. Rogers, Efthymia Kouvela et al.· Journal of Surgical Research· 0 citations
Sepsis remains a leading cause of mortality in intensive care units worldwide, a challenge exacerbated by pathophysiological and clinical heterogeneity that limits the effectiveness of uniform management strategies, motivating the development of phenotype-guided approaches to diagnosis and treatment. Artificial intelligence (AI)-assisted phenotyping can stratify patients with sepsis into distinct subpopulations with differential immune profiles and heterogeneous treatment responses. This narrative review synthesizes recent advances in AI-assisted sepsis phenotyping, focusing on three persistent research bottlenecks: terminological inconsistency, fragmented integration between prognostic stratification and treatment-response phenotyping, and ambiguous clinical translation pathways. A standardized nomenclature is proposed to improve cross-study comparability, accompanied by a delineation of mainstream AI methodological frameworks, critical care datasets, and multilevel validation systems tailored for intensive care unit scenarios. Eight complementary research dimensions are mapped, including transcriptomic endotyping, single-cell profiling, electronic health record-derived clinical phenotyping, dynamic trajectory modeling, organ dysfunction stratification, biomarker panels, multi-omic integration, and treatment-response phenotyping, with treatment-response phenotyping highlighted as the highest-priority translational frontier. Critical analysis of predominant translational barriers, such as limited model generalizability, insufficient interpretability, poor workflow compatibility, and regulatory uncertainty, is presented alongside stage-specific actionable roadmaps designed to promote real-world clinical deployment. By constructing a unified interdisciplinary framework, this review defines key future research priorities to accelerate the evidence-based transition from algorithmic prototypes to bedside precision sepsis management.
Hang Ruan, Jing-Kun Lee, Jie Xiong et al.· Digital Health· 0 citations
Vaccination stands as one of the most transformative interventions in the history of human civilization. In medicine, vaccination stands as a cornerstone that has saved countless lives across generations. Nevertheless, conventional vaccine development remains encumbered by prolonged timelines, substantial financial investment, and high attrition rates particularly during late-stage clinical trials underscoring the urgent need for more efficient and systematic approaches. In recent years, artificial intelligence (AI) has emerged as a transformative force across the biomedical sciences, offering unprecedented computational capacity to process and interpret complex biological datasets. The convergence of AI with vaccinology represents a significant methodological advancement which has the potential to fundamentally redefine the vaccine development paradigm. AI integrates advances in machine learning, multi-omics data analysis, and high-performance computing to accelerate antigen discovery, epitope prediction, immunogen design, and clinical evaluation. This development represents a paradigm shift toward faster, more precise, and scalable strategies for vaccine development. This review critically examines the current landscape of AI applications in vaccine development, with particular emphasis on recent advancements, translational challenges, and the prospective role of AI in shaping the future of immunization science.
Sastha N. Kumar, Rajhans Gondane, Shubham Mahindrakar et al.· Frontiers in Cellular and In...· 0 citations
Organoid biobanks represent a pivotal innovation at the intersection of stem cell biology, bioengineering, and precision medicine. By establishing standardized repositories of patient-derived organoids spanning diverse tissues and disease contexts, organoid biobanks enhance experimental reproducibility, foster international scientific collaboration, and accelerate translational research. This study presents a comprehensive bibliometric analysis of 1,318 publications over the past three decades and identifies transformative shifts in the field. Research output has increased substantially since 2014, driven by international consortia involving institutions in China, the United States, and Europe. The analysis highlights predominant applications in cancer modeling (particularly gastrointestinal and central nervous system malignancies), high-throughput drug screening, and regenerative medicine, with emerging frontiers in multi-organ system integration and artificial intelligence-enabled predictive modeling. Technological advances are increasingly being evaluated for clinical relevance through reported concordance between organoid-based predictions and patient treatment responses. While challenges persist in the functional maturation of organoids and in ethical governance, organoid biobanks are increasingly positioned to reshape biomedical research paradigms by bridging experimental models with clinical decision-making. The strategic development of standardized protocols and interdisciplinary frameworks will be essential to realize their full potential in advancing therapeutic discovery and personalized healthcare.
Yuxin Su, Yutian Feng, Qingru Song et al.· Organoid Research· 0 citations
Human brain organoids have evolved from early neurodevelopmental models into platforms for therapeutic discovery. Here, we highlight two cases in which organoid-derived findings enabled FDA-approved clinical trials. Patient-derived organoids modeling Pitt-Hopkins syndrome revealed human-specific, TCF4-dependent abnormalities and supported the development of a regulated AAV gene therapy. In parallel, Rett syndrome organoids cultured aboard the International Space Station uncovered space-induced neural senescence, characterized by retroelement-associated neuroinflammation, prompting evaluation of antiretroviral therapy. These examples illustrate how organoids can reveal disease mechanisms that are inaccessible or incompletely reproduced in animal models, while animal studies remain essential for validation and safety assessment. As the field advances, matching model complexity to experimental purpose-and ensuring reproducibility, scalability, and accessibility-will be critical. Human brain organoids are crossing a translational threshold, emerging as engines of therapeutic discovery and gateways to clinical intervention.
A. A. Martins, A. Muotri· Stem Cells and Development· 0 citations
