Evidence map›Paper›PMID 40536356›Full record

ReviewAnnals of neurology2025

AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.

Matthew Rizzo, Jeffrey D Dawson

Abstract readReview
In one paragraph

Review in Annals of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Turns and downturns in aging drivers.Transportation research interdisciplinary perspectives · 2026
    Article
  4. Review
  5. Large language models for neurology: a mini review.Frontiers in digital health · 2025
    Review
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

2 authors.

Matthew RizzoDepartment of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA.
Jeffrey D DawsonDepartment of Biostatistics, University of Iowa, Iowa City, IA, USA.

Funding

Tracking and Evaluation CoreU54GM115458 · NIGMS · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI HOLSTEIN, SARAH A · 2016 to 2025
$42.8M
PREDICTIONS OF DRIVER SAFETY IN ADVANCING AGER01AG017177 · NIA · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI RIZZO, MATTHEW · 1999 to 2024
$9.9M
NIA NIH HHS R01 AG017177NIA NIH HHS R01AG017177NIGMS NIH HHS U54 GM115458NIGMS NIH HHS U54GM115458University of Nebraska Foundation
6 · The paper itself

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications. We begin by tracing AI's origins in mathematics, human logic, and brain-inspired neural networks to establish a context for its development. The review highlights AI's growing role in neurological diagnostics and treatment, emphasizing machine learning applications, such as computer vision, brain-machine interfaces, and precision care. By mapping the evolution of AI tools and linking them to neuroscience and human reasoning, we illustrate how AI is reshaping neurological practice and research. We end the review with an overview of model selection in AI and a case scenario illustrating how AI may drive precision neurological care. Part 1 sets the stage for part 2, which will focus on practical applications of AI in real-world scenarios where humans and AI collaborate as joint cognitive systems. Part 3 will examine AI's integration with extensive healthcare and neurology networks, innovative clinical trials, and massive datasets, expanding our vision of AI's global impact on neurology, healthcare systems, and society. ANN NEUROL 2025;98:211-230.

Indexed as

Artificial IntelligenceNervous System DiseasesNeurologyBrain-Computer InterfacesHumansMachine Learning

Identifiers

PMID40536356
PMCPMC12278199

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.