Integrating Generative Artificial Intelligence in ADRD: A Roadmap for Streamlining Diagnosis and Care in Neurodegenerative Diseases
This addresses the problem of healthcare system strain in ADRD care for clinicians and patients, but it is incremental as it builds on existing AI concepts with a structured approach.
The paper tackles the challenge of meeting growing demand for neurological care in Alzheimer's disease and related dementias (ADRD) by proposing a six-phase roadmap for integrating LLM-based generative AI to enhance clinician capabilities, aiming to streamline diagnosis and care without specifying concrete numerical results.
Healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias (ADRD). We propose that LLM-based generative AI systems can enhance clinician capabilities to approach specialist-level assessment and decision-making in ADRD care at scale. This article presents a comprehensive six-phase roadmap for responsible design and integration of such systems into ADRD care: (1) high-quality standardized data collection across modalities; (2) decision support; (3) clinical integration enhancing workflows; (4) rigorous validation and monitoring protocols; (5) continuous learning through clinical feedback; and (6) robust ethics and risk management frameworks. This human centered approach optimizes clinicians' capabilities in comprehensive data collection, interpretation of complex clinical information, and timely application of relevant medical knowledge while prioritizing patient safety, healthcare equity, and transparency. Though focused on ADRD, these principles offer broad applicability across medical specialties facing similar systemic challenges.