Evidence map›Paper›PMID 40773081›Full record

ArticleAnnals of biomedical engineering2025

Clinical Application of Machine Learning in Biomedical Engineering for the Early Detection of Neurological Disorders.

Georgios P Georgiou

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Georgios P GeorgiouDepartment of Languages and Literature, University of Nicosia, Nicosia, Cyprus. georgiou.georg@unic.ac.cy.ORCID http://orcid.org/0000-0002-7192-2649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning is increasingly recognized as a transformative tool in the diagnosis and prognosis of neurodevelopmental, neurodegenerative, and learning disorders. Through the analysis of complex patterns in speech and language, these models may offer important insights that can support and enhance clinical decision-making. This paper explores the potential of machine learning to detect a range of disorders and discusses its key advantages, limitations, and clinical integration.

Indexed as

Biomedical EngineeringMachine LearningNervous System DiseasesEarly DiagnosisHumansClinicalLanguageMachine learningNeurological disordersSpeech

Identifiers

PMID40773081

What OpenQuestion holds

Textmetadata
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.