Evidence map›Paper›PMID 38827644›Full record

ArticleGalen medical journal2023

The Use of Artificial Intelligence in the Management of Neurodegenerative Disorders; Focus on Alzheimer's Disease.

Khazar Ghasempour Dabbaghi, Zahra Khosravirad, Sheida Jamalnia, Rahil GhorbaniNia, Fatemeh Mahmoudikohani, Habib Zakeri, Solmaz Khastehband

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Galen medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

7 authors.

Khazar Ghasempour DabbaghiSchool of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Zahra KhosraviradDepartment of Quality Improvement in Kowsar Hospital, Shiraz, Iran.
Sheida JamalniaDepartment of Nursing and Midwifery, Kazeroun Branch, Islamic Azad University, Kazeroun, Iran.
Rahil GhorbaniNiaNoncommunicable Disease Research Center, Bam University of Medical Science, Bam, Iran.
Fatemeh MahmoudikohaniDepartment of Midwifery, School of Nursing and Midwifery, Bam University of Medical Science, Bam, Iran.
Habib ZakeriResearch Center for Neuromodulation and Pain, NAB Pain Clinic, Shiraz University of Medical Sciences, Shiraz, Iran.
Solmaz KhastehbandDepartment of Educational Management, Islamic Azad University-south Tehran Branch, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in artificial intelligence (AI) have shown great promise in the diagnosis, prediction, treatment plans, and monitoring of neurodegenerative disorders. AI algorithms can analyze huge quantities of data from numerous sources, including medical images, quantifiable proteins in urine, blood, and cerebrospinal fluid (CSF), genetic information, clinical records, electroencephalography (EEG) signals, driving behaviors, and so forth. Alzheimer's disease (AD) is one of the most common neurodegenerative disorders that progressively damage cognitive abilities and memory. This study specifically explores the possible application of AI in the diagnosis, prediction, monitoring, biomarker or drug discovery, and classification of AD.

Indexed as

AlzheimerArtificial IntelligenceNeurodegenerative Diseases

Identifiers

PMID38827644
PMCPMC11144027

What OpenQuestion holds

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Registered trials

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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.