Integrating Generative Artificial Intelligence in ADRD: A Framework for Streamlining Diagnosis and Care in Neurodegenerative Diseases

• DOI: 10.48550/arxiv.2502.06842
• Publication Date: 2025-02-06
• Authors: Andrew Breithaupt; Alice Tang; Bruce L. Miller; Pedro Pinheiro‐Ch...
• Keywords: FOS: Computer and information sciences; Computer Science - Computers and Society; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computers and Society (cs.CY); I.2.1

Healthcare systems are struggling to meet the growing demand for neurological care, with challenges particularly acute in Alzheimer's disease and related dementias (ADRD). While artificial intelligence research has often focused on identifying patterns beyond human perception, implementing such predictive capabilities remains challenging as clinicians cannot readily verify insights they themselves detect. We propose that large language models (LLMs) offer more immediately practical applications by enhancing clinicians' three critical areas: comprehensive data collection, interpretation of complex clinical information, timely application relevant medical knowledge. These stem from limited time proper diagnosis, complexity, an overwhelming volume literature exceeds any clinician's capacity fully master. present a framework responsible AI integration leverages LLMs' ability communicate effectively both patients providers while maintaining oversight. This approach prioritizes standardized, high-quality collection enable system learns every patient encounter incorporating latest evidence, continuously improving care delivery. begin address implementation initiate important discussions around ethical considerations governance needs. developed ADRD, this roadmap provides principles across neurology other specialties, potential improve diagnostic accuracy, reduce disparities, advance knowledge through learning healthcare system.