Towards an AI biomedical scientist: Accelerating discoveries in neurodegenerative disease
Despite major advances in Alzheimer's disease and related diseases (ADRD) research, the translation of discoveries into impactful clinical interventions remains slow. Overwhelming data complexity, fragmented knowledge, and prolonged research cycles hinder progress in understanding and treating neurodegenerative diseases. Artificial intelligence (AI) offers a promising path forward, particularly when developed as a scientist-in-the-loop system that collaborates with researchers throughout the scientific discovery process. This paper introduces the concept of an AI Biomedical Scientist, an intelligent platform designed to support literature synthesis, hypothesis generation, experimental design, and data interpretation. This platform aims to function as a holistic scientific partner, integrating diverse biomedical data and expert reasoning to accelerate discovery. We review commercial and academic efforts and introduce targeted Minimum Viable Products (MVPs) needed for general biomedical research lab utilization of AI, such as robust and accurate tools for literature and data analysis, negative data models, and virtual peer review, with a longer-term vision of foundation models trained directly on biomedical datasets. In AD and neurodegeneration research, such tools are anticipated to deliver efficiency gains ranging from modest improvements in specific research tasks to potential multi-fold accelerations in discovery workflows as systems mature and scale. This review examines the technical foundations, challenges, and anticipated impacts of AI and aims to inform and engage researchers in utilizing these systems to transform biomedical discovery, starting with AD and extending to other complex conditions.
Authors
- Gregory J. Moore (ORCID: https://orcid.org/0000-0001-8541-3194)
- Luca Foschini (ORCID: https://orcid.org/0000-0003-1409-3570)
- Yannis Kevrekidis
- Kaleigh F. Roberts (ORCID: https://orcid.org/0000-0001-6483-9153)
- Thomas Sandmann (ORCID: https://orcid.org/0000-0002-6601-8890)
- Zachary B. Abrams (ORCID: https://orcid.org/0000-0001-5219-9996)
- Cassie S. Mitchell
- Mukta Phatak (ORCID: https://orcid.org/0009-0003-2126-3723)
- S. Pablo Sardi (ORCID: https://orcid.org/0000-0001-9805-3677)
- Randall John Bateman (ORCID: https://orcid.org/0000-0002-7729-1702)
- Niranjan Bose (ORCID: https://orcid.org/0000-0002-1301-7827)
- Ludovico Mitchener (ORCID: https://orcid.org/0000-0002-5968-9776)
- Rande H. Lazar
- Mingfang Zhu (ORCID: https://orcid.org/0000-0001-8379-2178)
- Ron Mallon (ORCID: https://orcid.org/0000-0001-8347-8939)
- Gayle Wittenberg (ORCID: https://orcid.org/0009-0004-4299-5138)
- Donald L. Elbert (ORCID: https://orcid.org/0000-0001-6480-9536)
- Martin Voss (ORCID: https://orcid.org/0000-0002-5221-4647)
- Towfique Raj (ORCID: https://orcid.org/0000-0002-9355-5704)
- Gustavo Jimenez‐Maggiora (ORCID: https://orcid.org/0000-0001-9745-5121)
- Albert M. Lai (ORCID: https://orcid.org/0000-0002-9241-2656)
- Payam Barnaghi (ORCID: https://orcid.org/0000-0001-8591-9638)
- John F. Crary (ORCID: https://orcid.org/0000-0002-0556-293X)
- Rajaraman Krishnan (ORCID: https://orcid.org/0000-0002-5189-4097)
- Sihui Song
- Eric C. Landsness (ORCID: https://orcid.org/0000-0003-3227-1641)
- Justin Reese (ORCID: https://orcid.org/0000-0002-2170-2250)
- Srinivas Koutarapu (ORCID: https://orcid.org/0000-0002-4355-6733)
- Gabrielle Strobel
- Justin Melendez (ORCID: https://orcid.org/0000-0003-3629-3274)
- Chenguang Wang (ORCID: https://orcid.org/0000-0002-9082-2117)
- Yixin Chen (ORCID: https://orcid.org/0000-0001-5049-0202)
- Yun Chen (ORCID: https://orcid.org/0000-0003-0547-9463)
- Elizabeth Wu (ORCID: https://orcid.org/0000-0002-1260-2952)
- Joel Schwartz
Institutions
- Georgia Institute of Technology (US)
- Allen Institute for Brain Science (US)
- Booz Allen Hamilton (United States) (US)
- Sage Bionetworks (US)
- Johnson & Johnson (United States) (US)
- Johns Hopkins University (US)
- Lawrence Berkeley National Laboratory (US)
- University of California, Santa Cruz (US)
- Washington University in St. Louis (US)
- Biomedical Research Networking Center on Neurodegenerative Diseases (ES)
- Bristol-Myers Squibb (United States) (US)
- Denali Therapeutics (United States) (US)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Alzheimer’s Disease Neuroimaging Initiative (US)
- Sanofi (United States) (US)
- Institute for the Future (US)
- Alzforum (US)
- Future Earth (CA)
- Artificial Intelligence in Medicine (Canada) (CA)
- Imperial College London (GB)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- The Journal of Prevention of Alzheimer s Disease
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.tjpad.2025.100398
- Primary Topic
- Health, Environment, Cognitive Aging
- Type
- article
- Field-Weighted Citation Impact
- 0.00