Areas of Focus:

Biomarker DiscoveryHuman CohortsImmune ProfilingT Cell BiologyTranslational & ClinicalAnti-Myelin Oligodendrocyte Glycoprotein Antibody Disease (MOG)Multiple SclerosisNeurologic DiseasesNeuromyelitis Optica (NMO)
  • Associate Professor, Tel Aviv Sourasky Medical Center, Tel Aviv University
    School of Continuing Medical Education (Neurology)

Dr. Arnon Karni is an Associate Professor in the Faculty of Medicine at Tel Aviv University and a leading neurologist and neuroimmunologist at Tel Aviv Sourasky Medical Center. He earned his medical degree from Tel Aviv University School of Medicine and completed advanced immunological studies at Harvard University.

Dr. Karni is an internationally recognized expert in multiple sclerosis, myasthenia gravis, neuromyelitis optica, and other complex neurological and autoimmune conditions, with over 22 years of clinical and research experience. His research focuses on the role of immune cells in neurological disease, with a current emphasis on developing a multi-parameter prognostic prediction model for disability and disease progression in early-stage multiple sclerosis. He leads and collaborates on national and international research networks and has authored more than 30 peer-reviewed publications and participated in over 40 international scientific conferences.

Dr. Karni is a member of the Israeli Society of Neurology, the Israeli Society of Neuroimmunology, and the European Association for Multiple Sclerosis Treatment and Research.

Projects

Featured Pilot Projects

Developing a Multi-parameter Prognostic Prediction Model for Disability Ranks and Progression of Patients with Multiple Sclerosis at the Early Stages of the Disease
Project | Tel Aviv University

Developing a Multi-parameter Prognostic Prediction Model for Disability Ranks and Progression of Patients with Multiple Sclerosis at the Early Stages of the Disease

Integrating clinical, imaging, and biological data from large real-world MS cohorts, this project builds a machine learning model to predict disability progression early and enable personalized treatment decisions.