ReviewSeminars in neurology2026
Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.
Review in Seminars in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
As the U.S. population ages, Alzheimer's disease and related dementias (ADRD) cases are increasing, resulting in long wait times for specialist care. We review state-of-the-art artificial intelligence (AI) applications in ADRD care, from streamlining clinical diagnosis to pioneering novel digital biomarkers. Near-term AI applications include neuroimaging interpretation, conversational agents for patient interviews, and digital cognitive assessments. Large language models show promise as collaborative partners, helping clinicians interpret complex data while supporting patients and caregivers. Emerging digital biomarkers-speech analysis, passive monitoring through wearable devices, electronic health record analysis, and multiomics-offer potential for continuous monitoring to detect cognitive decline years before traditional assessments. Despite the acceleration of AI innovation, most of these systems are inaccessible in clinical practice. Implementation bottlenecks include limited external validation, technical challenges, model biases, infrastructure, and regulatory requirements. This review aims to help neurologists navigate this rapidly evolving AI landscape and prepare for implementation in ADRD care.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.