Artificial Intelligence-Based Detection of ARIA on MRI During Alzheimer Disease Therapy: Expert Opinion on Responsible Clinical Integration.
Authors
Affiliations (9)
Affiliations (9)
- Professor of Radiology, Division of Neuroradiology; Director, Alzheimer Disease Imaging Research Laboratory, Duke University School of Medicine, Durham, NC, USA.
- Professor of Neuroradiology, Queen Square Institute of Neurology and UCL Hawkes Institute, University College London, London UK; Dept of Radiology & Nuclear Medicine, Amsterdam UMC, location Vrije Universiteit, Amsterdam, the Netherlands.
- Hugh Monroe Wilson Professor of Radiology; Chief of MRI Service, Mallinckrodt Institute of Radiology; Professor of Radiology and Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- Associate Professor of Radiology, Mayo Clinic, Rochester, MN, USA.
- Associate Professor of Radiology; Director of Dementia Imaging and Molecular Neuroimaging, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell; Feinstein Institutes for Medical Research, New York, NY, USA.
- Moreno Family Endowed Chair for Alzheimer's Research, Professor of Neurology, Director, Applied Alzheimer's Research Laboratory, Barrow Neurological Institute; Vice Chair for Clinical Research, Creighton University School of Medicine, Phoenix AZ, USA.
- Professor of Psychiatry and Human Behavior and Professor of Neurology, Warren Alpert Medical School of Brown University; Director, Memory and Aging Program at Butler Hospital, Providence, RI, USA.
- Professor of Radiology (Neuroimaging and Neurointervention), Stanford University, Stanford Healthcare, Stanford, CA, USA.
- Professor of Psychiatry and Professor in Medicine; Director, Neurocognitive Disorders Program, Duke University School of Medicine, Durham, NC, USA.
Abstract
Amyloid-targeted monoclonal antibody therapies have introduced a new era in the treatment of early Alzheimer disease. However, their use has increased the importance of detecting and monitoring amyloid-related imaging abnormalities (ARIA) on MRI, as such findings may influence treatment continuation, dose modification, and patient safety assessment. As anti-amyloid therapies expand into routine practice, increasing surveillance MRI volumes, interreader variability, and the potential for missed subtle abnormalities have generated interest in artificial intelligence (AI)-based clinical decision support tools. A multidisciplinary panel of neuroradiologists and Alzheimer disease clinicians examined the extent of evidence supporting clinical implementation of AI-assisted ARIA detection tools, these tools' safe integration into practice, and remaining evidence gaps. The panel concluded that AI-assisted ARIA detection is likely to enhance patient safety when used as clinical decision support within a radiologist-in-the-loop framework. Panelists also noted substantial variation among commercially available tools in regulatory status, technical capabilities, and validation evidence. Moreover, they emphasized the need for further studies to assess the impact of improved detection on clinical outcomes. Overall, the panel supported conditional implementation with radiologist oversight, ongoing quality assurance, and prospective monitoring of clinical performance.