INTRO
Meet Dr. Deborah Blacker, Co-Lead of the AD/ADRD Pilot Core at MassAITC. In her day job, she wears multiple hats: Professor of Psychiatry at Harvard Medical School, Leader of the Research Education Core and Co-Leader of the Clinical Core at Massachusetts Alzheimer’s Disease Research Center (MADRC), and Associate Chief for Research in Psychiatry at Massachusetts General Hospital. Dr. Blacker's research is a comprehensive exploration into the epidemiology, genetics, and early recognition of Alzheimer’s disease and related disorders. She collaborates extensively with experts in epidemiology, bioinformatics, and health policy, aiming to optimize methods for measuring dementia in electronic health records (EHRs). She also serves as Director of the Gerontology Research Unit at Massachusetts General Hospital and has contributed to the development of new methods for the genetic study of complex diseases. Join us as we delve into Dr. Blacker's insights on the potential of AI to revolutionize measurement and diagnosis in the field of aging and dementia.
#1 - Can you share with us a little about your work or research?
My research focuses on epidemiology, genetics, phenomenology, assessment, and early recognition of Alzheimer’s disease and related disorders, with a particular focus on how assessment methods and sampling affect research findings. I see patients in MADRC’s Memory Disorders Unit and evaluate research subjects in the MADRC Clinical Core and have multiple collaborations with a variety of epidemiology, bioinformatics, and health policy investigators on optimal methods for measuring dementia in electronic health records and claims.
#2 - What initially drew you to this intersection of AI, AgeTech, aging, and dementia? Is there a personal story or motivation behind your commitment to this field?
My involvement grew out of my interest in measurement issues, both in the possibility of technology in general and artificial intelligence (AI) in particular to enhance measurement in patients and research subjects with remote (and repeated) assessment and digital phenotyping, and in the use of AI to improve our ability to make diagnosis in EHRs—which is useful directly and for better validation of claims-based methods and understanding of their limitations.
#3 - In your view, where is the biggest gap in the current landscape of aging and dementia research and care, and how can AI and emerging technologies help bridge this?
Certainly one of the large gaps, which affects both the power of our clinical trials and our ability to monitor patients at home, is the instability of our measures. There are multiple potential contributions of AI and technology, from simple app-based measures that can be done at home to a variety of passive digital phenotyping approaches to improved EHR-based diagnosis for large-sample observational studies (e.g., drug repurposing).
#4 - Any words of wisdom for budding startups or researchers eager to dive into the AI and AgeTech space?
Be wary of the limitations as well as the potential promise of your methods. Consider how variation in sensory and motor abilities and socioeconomic factors may affect your measures, and be aware that the digital divide across economic and racial/ethnic groups is larger among older individuals.
#5 - What's the most constructive piece of criticism or feedback you've received in your career, and how did it shape your research or business trajectory?
Don't make it (your thinking or your prose) too complicated—remember that whoever is reading this is juggling a million other things. This is common advice, but no less important for that. It has made me a better grant writer—and I hope has helped me help others as well—both directly and by passing it on.
