Public articles linked to the same research event.
Clinical transplantation and research This review synthesizes current evidence on the use of artificial intelligence and machine learning in preclinical nonhuman primate xenotransplantation, noting that computer vision can support continuous noninvasive behavioral phenotyping and pain assessment, anomaly detection algorithms can extract early warning signals from biosignal streams, multiomics integration can identify xenograft-specific biomarker signatures, digital twin frameworks may enable hypothesis-generating simulations of prospective human recipient responses, and explainable AI can make model outputs more transparent and regulatory defensible, and it proposes a research agenda through which AI-augmented nonhuman primate experimentation can become a bridge from preclinical data complexity to clinical precision in xenotra
This review synthesizes current evidence on the use of artificial intelligence and machine learning in preclinical nonhuman primate xenotransplantation, noting that computer vision can support continuous noninvasive behavioral phenotyping and pain assessment, anomaly detection algorithms can extract early warning signals from biosignal streams, multiomics integration can identify xenograft-specific biomarker signatures, digital twin frameworks may enable hypothesis-generating simulations of prospective human recipient responses, and explainable AI can make model outputs more transparent and regulatory defensible, and it proposes a research agenda through which AI-augmented nonhuman primate experimentation can become a bridge from preclinical data complexity to clinical precision in xenotra
This review synthesizes current evidence on the use of artificial intelligence and machine learning in preclinical nonhuman primate xenotransplantation, noting that computer vision can support continuous noninvasive behavioral phenotyping and pain assessment, anomaly detection algorithms can extract early warning signals from biosignal streams, multiomics integration can identify xenograft-specific biomarker signatures, digital twin frameworks may enable hypothesis-generating simulations of prospective human recipient responses, and explainable AI can make model outputs more transparent and regulatory defensible, and it proposes a research agenda through which AI-augmented nonhuman primate experimentation can become a bridge from preclinical data complexity to clinical precision in xenotra
This review synthesizes current evidence on the use of artificial intelligence and machine learning in preclinical nonhuman primate xenotransplantation, noting that computer vision can support continuous noninvasive behavioral phenotyping and pain assessment, anomaly detection algorithms can extract early warning signals from biosignal streams, multiomics integration can identify xenograft-specific biomarker signatures, digital twin frameworks may enable hypothesis-generating simulations of prospective human recipient responses, and explainable AI can make model outputs more transparent and regulatory defensible, and it proposes a research agenda through which AI-augmented nonhuman primate experimentation can become a bridge from preclinical data complexity to clinical precision in xenotra