Artificial Intelligence-Based Detection of ARIA on MRI During Alzheimer Disease Therapy: Expert Opinion on Responsible Clinical Integration.

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.