Research
At the NeuroAI Lab, we bridge powerful AI with cognitive neuroscience to streameline the discovery, diagnosis and care of neurodegenerative diseases. Check out our perspective paper on Integrating Generative Artificial Intelligence in ADRD: A Roadmap for Streamlining Diagnosis and Care in Neurodegenerative Diseases
- Foundation AI Models using vast multimodal datasets to accelerate disease discovery.
- Decision Support Systems to scale specialist-level clinical and neuropathological diagnoses.
- AI Conversational Agents to conduct clinical assessments, streamlining data collection, monitoring and care.
Foundation AI Models
We are building foundation models using massive clinical datasets, such as electronic medical record (EMR), by combining state-of-the-art generative AI and traditional machine learning algorithms. By integrating structured EMR data (diagnoses, labs, medications, imaging) with unstructured information from millions of clinical notes parsed with LLMs, we aim to identify clinical subtypes, key symptoms, and risk factors that inform disease onset, progression, and differential diagnosis. We recently applied our model to characterize the clinical and biological heterogeneity of Alzheimer’s disease (AD) and frontotemporal dementia (FTD). The project further leverages the UCSF MAC’s rich longitudinal cohort to validate data-driven discoveries. Ultimately, this work aims to develop clinically actionable models and decision support systems that enhance early diagnosis and guide individualized care. See our preprint here: Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning. Main collaborators: Alice Tang, PhD and Marina Sirota, PhD.
Decision Support Systems
Decision support systems leverage foundation models to deliver specialist-level diagnoses at scale. The Memory and Aging Center Copilot is an AI-powered decision support system that integrates multimodal patient data—including unstructured clinical notes, neuropsychological assessments, and structural MRI scans—via a scientifically grounded, modular multi-agent architecture. The system leverages advanced generative AI paired with discriminative machine learning classifiers trained on a large, deeply phenotyped cohort to deliver clinical and neuropathological diagnoses at specialist-level accuracy and at a fraction of the time typically required. The Copilot’s interactive chat-based interface enables clinicians to dynamically query both the model’s reasoning and underlying patient data, supporting collaborative decision-making. This project was recently awarded a UCSF Catalyst Award (a translational accelerator focused on advancing UCSF discoveries for patient benefit and commercialization). See our preprint here: Agentic Generative Artificial Intelligence System for Classification of Pathology-Confirmed Primary Progressive Aphasia Variants. Main collaborators: Bruce Miller, MD; Katherine Possin, PhD; Maria Luisa Gorno-Tempini, MD, PhD; Bill Seeley, MD; and Andreas Rauschecker, MD, PhD.
AI Conversational Agents
AI Conversational Agents can be deployed by decision support systems when clinical information is incomplete or to extend care to underserved areas. We are developing and validating the voice Clinical Dementia Rating (vCDR), an automated and scalable digital version of the Clinical Dementia Rating (CDR®)—a widely used global benchmark for staging dementia severity and tracking cognitive and functional decline in Alzheimer’s disease and Related Dementias. The CDR is administered in-clinic through a semi-structured interview with both the participant and a study partner, whose combined perspectives are essential for assessing changes from an individual’s baseline abilities. The vCDR leverages generative AI (large language models) to conduct these interviews, automate scoring, and optimize the analysis of additional features, including connected speech. The vCDR aims to provide a more sensitive, consistent, and accessible tool for detecting and monitoring dementia progression, validating it as a clinical trial endpoint, and laying the groundwork for inclusive, global dementia research and care. Main collaborators: Michael Weiner, PhD; Rachel Nosheny, PhD; John Morris, MD; and Jet Vonk, PhD.
Support
We are generously supported by the NIH, Eli Lilly, Gates Ventures,UCSF Dyslexia Center and the UCSF Multitudes.
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