Evidence map›Paper›PMID 42533108›Full record

ReviewNature aging2026

Agentic AI for scaling diagnosis and care in neurodegenerative disease.

Andrew G Breithaupt, Michael Weiner, Alice Tang, Katherine L Possin, Marina Sirota, James Lah, Allan I Levey, Pascal Van Hentenryck, Reza Zandehshahvar, Marilu Luisa Gorno-Tempini and 7 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Andrew G BreithauptGoizueta Brain Health Institute, Emory University, Atlanta, GA, USA. andrew.g.breithaupt@emory.edu.ORCID http://orcid.org/0000-0003-3955-5428
Michael WeinerDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Alice TangSchool of Medicine, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-4745-0714
Katherine L PossinMemory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-7246-6083
James LahGoizueta Brain Health Institute, Emory University, Atlanta, GA, USA.ORCID http://orcid.org/0000-0002-8810-620X
Allan I LeveyGoizueta Brain Health Institute, Emory University, Atlanta, GA, USA.ORCID http://orcid.org/0000-0002-3153-502X
Pascal Van HentenryckNSF AI Institute for Advances in Optimization (AI4OPT), Georgia Institute of Technology, Atlanta, GA, USA.ORCID http://orcid.org/0000-0001-7085-9994
Reza ZandehshahvarNSF AI Institute for Advances in Optimization (AI4OPT), Georgia Institute of Technology, Atlanta, GA, USA.ORCID http://orcid.org/0000-0002-6249-905X
Marilu Luisa Gorno-TempiniMemory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Joseph GiorgioDepartment of Neuroscience, University of California, Berkeley, Berkeley, CA, USA.ORCID http://orcid.org/0000-0003-3704-5977
Jingshen WangDivision of Biostatistics, University of California, Berkeley, Berkeley, CA, USA.
Andreas M RauscheckerDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Howard J RosenMemory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Rachel L NoshenyDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Bruce L MillerMemory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Pedro Pinheiro-ChagasMemory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA. pedro.pinheirochagas@ucsf.edu.ORCID http://orcid.org/0000-0001-8512-0113

Funding

Alzheimer's Association NAAmerican Brain Foundation (ABF) NAEisai NANational Science Foundation (NSF) 2112533
6 · The paper itself

Abstract

US healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias. Generative artificial intelligence (AI) built on large language models now enables agentic AI systems that can streamline clinical workflows, integrate multimodal data and learn from practicing specialists. We envision an agentic AI system that scales specialist-level care to nonspecialist clinical settings through a continuously learning healthcare system. We describe this destination and outline a phased roadmap for responsible design and integration into care of Alzheimer's disease and related dementias: (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.

Indexed as

Alzheimer DiseaseArtificial IntelligenceNeurodegenerative DiseasesDelivery of Health CareGenerative Artificial IntelligenceHumansLarge Language Models

Identifiers

PMID42533108

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.