Evidence map›Paper›PMID 37332002›Full record

ArticleAnnals of biomedical engineering2023

The Potential of ChatGPT in Assisting Children with Down Syndrome.

Jing Yang, Lip Yee Por, Ming Chern Leong, Chin Soon Ku

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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.

Jing YangDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
Lip Yee PorDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia. porlip@um.edu.my.
Ming Chern LeongPaediatric & Congenital Heart Centre, Institut Jantung Negara, 145, Jalan Tun Razak, 51200, Kuala Lumpur, Malaysia. mcleong@ijn.com.my.
Chin Soon KuDepartment of Computer Science, Universiti Tunku Abdul Rahman, 31900, Kampar, Malaysia. kucs@utar.edu.my.ORCID http://orcid.org/0000-0003-0793-3308

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ChatGPT, an advanced language generation model developed by OpenAI, has the potential to revolutionize healthcare delivery and support for individuals with various conditions, including Down syndrome. This article explores the applications of ChatGPT in assisting children with Down syndrome, highlighting the benefits it can bring to their education, social interaction, and overall well-being. While acknowledging the challenges and limitations, we examine how ChatGPT can be utilized as a valuable tool in enhancing the lives of these children, promoting their cognitive development, and supporting their unique needs.

Indexed as

Artificial IntelligenceDown SyndromeChildHumansChatGPTDown syndromeEducationLearningSocial interaction

Identifiers

PMID37332002

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.