Evidence map›Paper›PMID 39956906›Full record

ReviewNeurological research and practice2025

AI and Neurology.

Julian Bösel, Rohan Mathur, Lin Cheng, Marianna S Varelas, Markus A Hobert, José I Suarez

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Neurological research and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
  3. Article
  4. Review
  5. Review
  6. 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

6 authors.

Julian BöselDepartment of Neurology, University Hospital Heidelberg, Heidelberg, Germany. mail@julian-boesel.de.ORCID http://orcid.org/0000-0003-2326-0473
Rohan MathurDivision of Neurosciences Critical Care, Departments of Neurology, Anesthesiology & Critical Care Medicine, Johns Hopkins University Hospital and School of Medicine, Baltimore, MD, USA.
Lin ChengDivision of Neurosciences Critical Care, Departments of Neurology, Anesthesiology & Critical Care Medicine, Johns Hopkins University Hospital and School of Medicine, Baltimore, MD, USA.
Marianna S VarelasDepartment of Neurology, Albany Medical College, Albany, NY, USA.
Markus A HobertDepartment of Neurology, University Hospital Schleswig-Holstein Campus Kiel and Christian-Albrechts-University of Kiel, Kiel, Germany.
José I SuarezDivision of Neurosciences Critical Care, Departments of Neurology, Anesthesiology & Critical Care Medicine, Johns Hopkins University Hospital and School of Medicine, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials. MAIN BODY: In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise.

conclusionArtificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.

Indexed as

AIArtificial intelligenceData-driven medicineDeep learningMachine learningNeural networksNeurology

Identifiers

PMID39956906
PMCPMC11921979

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.