The AI for Healthcare and Biomedicine (AIHB) Research Centre unites research expertise across XJTLU’s Suzhou Industrial Park (SIP) campus and Taicang base. Specialists in artificial intelligence, medical imaging, bioinformatics, affective computing, biomedical engineering, human-computer interaction, and data science collaborate to address major challenges in healthcare and biomedicine.
AIHB aims not only to develop more accurate AI algorithms, but also to uncover the biological and clinical insights they reveal. This includes assessing whether results remain reliable across hospitals, medical imaging machines, and populations, and evaluating how they contribute to real clinical, biomedical, or care pathways. Research spans multiple biological and operational scales – from genes, RNA modification, and single cells; to pathology, radiology, ultrasound, and physiological signals; and further to patient outcomes, treatment monitoring, functional rehabilitation, and emotional well-being.
Members contribute substantial foundations in prospective and multicentre medical AI, digital and computational pathology, cardiovascular CT and MRI, fetal ultrasound, research and visual-function assessment, cancer bioimaging, single-cell epitranscriptomics, multimodal depression assessment, affective intelligence, medical visualisation, rehabilitation systems, medical robotics, and biofabrication. Members have published in Nature Medicine, Cell Genomics, Nature Communications, Medical Image Analysis, IEEE Transactions on Medical Imaging, IEEE Transactions on Affective Computing, MICCAI (Medical Image Computing and Computer Assisted Intervention), AAAI (Association for the Advancement of Artificial Intelligence), and other leading journals and conferences. Members have also undertaken projects with government bodies, hospitals, and industry, resulting in patents and a foundation for clinical and industrial collaboration.
AIHB offers collaborators an integrated route from problem definition and data design to algorithm development, mechanistic interpretation, prototype construction, and external validation. It particularly welcomes hospitals with well-defined clinical cohorts, biomedical laboratories with multi-omics or experimental capabilities, pharmaceutical and medical technology companies, rehabilitation organizations, and AI or data partners seeking medically grounded research and responsible translation.
Centre overview
The AI for Healthcare and Biomedicine (AIHB) Research Centre unites research expertise across XJTLU’s Suzhou Industrial Park (SIP) campus and Taicang base. Specialists in artificial intelligence, medical imaging, bioinformatics, affective computing, biomedical engineering, human-computer interaction, and data science collaborate to address major challenges in healthcare and biomedicine.
AIHB aims not only to develop more accurate AI algorithms, but also to uncover the biological and clinical insights they reveal. This includes assessing whether results remain reliable across hospitals, medical imaging machines, and populations, and evaluating how they contribute to real clinical, biomedical, or care pathways. Research spans multiple biological and operational scales – from genes, RNA modification, and single cells; to pathology, radiology, ultrasound, and physiological signals; and further to patient outcomes, treatment monitoring, functional rehabilitation, and emotional well-being.
Members contribute substantial foundations in prospective and multicentre medical AI, digital and computational pathology, cardiovascular CT and MRI, fetal ultrasound, research and visual-function assessment, cancer bioimaging, single-cell epitranscriptomics, multimodal depression assessment, affective intelligence, medical visualisation, rehabilitation systems, medical robotics, and biofabrication. Members have published in Nature Medicine, Cell Genomics, Nature Communications, Medical Image Analysis, IEEE Transactions on Medical Imaging, IEEE Transactions on Affective Computing, MICCAI (Medical Image Computing and Computer Assisted Intervention), AAAI (Association for the Advancement of Artificial Intelligence), and other leading journals and conferences. Members have also undertaken projects with government bodies, hospitals, and industry, resulting in patents and a foundation for clinical and industrial collaboration.
AIHB offers collaborators an integrated route from problem definition and data design to algorithm development, mechanistic interpretation, prototype construction, and external validation. It particularly welcomes hospitals with well-defined clinical cohorts, biomedical laboratories with multi-omics or experimental capabilities, pharmaceutical and medical technology companies, rehabilitation organizations, and AI or data partners seeking medically grounded research and responsible translation.
Centre Director
Dr Kang Dang | Director of AIHB
Email: Kang.Dang@xjtlu.edu.cn