Evidence map›Paper›PMID 35203524›Full record

ReviewBiomedicines2022

The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.

Anuschka Silva-Spínola, Inês Baldeiras, Joel P Arrais, Isabel Santana

Abstract readReview
In one paragraph

Review in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

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

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  18. Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
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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

4 authors.

Anuschka Silva-SpínolaUniv Coimbra, Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal.
Inês BaldeirasUniv Coimbra, Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal.ORCID 0000-0002-8106-7308
Joel P ArraisUniv Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal.ORCID 0000-0003-4937-2334
Isabel SantanaUniv Coimbra, Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal.

Funding

Fundação para a Ciência e Tecnologia DFA/BD/6393/2020Fundação para a Ciência e Tecnologia UIDB/04539/2020: CIBB
6 · The paper itself

Abstract

Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer's disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease-patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.

Indexed as

AD modelsAlzheimer’s diseaseartificial intelligencedata sciencemachine learning

Identifiers

PMID35203524
PMCPMC8869403

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.