Evidence map›Paper›PMID 41402643›Full record

ReviewPediatric research2025

The evolving role of machine learning in autism spectrum disorder: current evidence and future directions.

Khaled Saad, Soha A Hussain, Ahmad Roshdy Ahmad, Mohamed Shawky Elfarargy, Amira Elhoufey, Abdulrahman A Al-Atram, Abdelrahman N Abdelal, Kawashty R Mohamed, Mostafa M Embaby

Abstract readReview
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In one paragraph

Review in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Khaled SaadDepartment of Pediatrics, Assiut University, Assiut, Egypt. ksaad8@yahoo.com.ORCID 0000-0002-8473-6116
Soha A HussainDepartment of Pediatrics, Cairo University, Cairo, Egypt.
Ahmad Roshdy AhmadDepartment of Pediatrics, College of Medicine, Jouf University, Sakaka, Saudi Arabia.
Mohamed Shawky ElfarargyDepartment of Pediatrics, College of Medicine, Jouf University, Sakaka, Saudi Arabia.
Amira ElhoufeyDepartment of Community Health Nursing, Alddrab University College, Jazan University, Jazan, Saudi Arabia.
Abdulrahman A Al-AtramDepartment of Psychiatry, College of Medicine, Majmaah University, Al Majmaah, Saudi Arabia.
Abdelrahman N AbdelalDepartment of Neurology, Al-Azhar University, Assiut, Egypt.
Kawashty R MohamedDepartment of Neurology, Al-Azhar University, Assiut, Egypt.
Mostafa M EmbabyDepartment of Pediatrics, Assiut University, Assiut, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

impactMachine learning (ML) has become a key factor in advancing artificial intelligence (AI)-driven strategies across various areas in recent years, including screening, diagnosis, subtyping, and therapeutic intervention in autism spectrum disorder (ASD). These technological advancements collectively demonstrate ML's potential to complement-rather than replace-expert clinical assessment in the screening and diagnosis of ASD. Future research should focus on standardizing data collection procedures, improving the interpretability of models, and conducting multi-center validation studies to confirm their effectiveness and applicability in real-world settings.

Indexed as

Autism Spectrum DisorderMachine LearningArtificial IntelligenceChildHumans

Identifiers

PMID41402643

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.