Bibliometric Examination of Artificial Intelligence Studies on Infants.
The infancy period is a critical stage in which neurobiological, motor, cognitive and socio‐emotional development progresses most rapidly and sensitively. Therefore, artificial intelligence applications make an important contribution to the more sensitive monitoring of early developmental indicators...
| Publicado en: | Infant & Child Development Vol. 35; no. 2; pp. 1 - 12 |
|---|---|
| Autor principal: | |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193280274&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193280274 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15227227 BW5 jtl: Infant & Child Development issn: 15227227 maglogo: Y pubinfo: dt: Mar2026 vid: 35 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 193280274 193280274 193280274 10.1002/icd.70098 193280274 ppf: 1 ppct: 11 formats: tig: atl: Bibliometric Examination of Artificial Intelligence Studies on Infants. aug: au: Yigit, Deniz affil: Department of Child Health and Diseases Nursing, Faculty of Health Sciences, Kütahya Health Sciences University, Kütahya, Turkey sug: subj: Artificial Intelligence Utilization Infant Development Evaluation Human Descriptive Research Bibliometrics Citation Analysis Data Analysis Software Thematic Analysis Descriptive Statistics Machine Learning Electrocardiography Magnetic Resonance Imaging Ensemble Learning Cardiotocography Collaboration Early Diagnosis Infant Development Disorders Diagnosis Diagnosis, Computer Assisted Risk Assessment Infant Development Disorders Risk Factors Early Childhood Intervention ab: The infancy period is a critical stage in which neurobiological, motor, cognitive and socio‐emotional development progresses most rapidly and sensitively. Therefore, artificial intelligence applications make an important contribution to the more sensitive monitoring of early developmental indicators related to infants. The aim of this study was to conduct a bibliometric analysis of studies on artificial intelligence on infants. The study is a descriptive study using bibliometric analysis. In the study, 1380 publications obtained from the Web of Science Core Collection database using the keywords 'infant and artificial intelligence' were analysed. R programme and Biblimotrix–Biblioshiny programme were used for data analysis. After the analysis, the findings are presented under four headings: main information, word cloud, trending topics and thematic map. Publications on the subject cover the period between 1983 and 2025, with an average publication age of 5.19 years. The annual growth rate of these publications is 9.18%. Published by a very large number of different authors (10,554), each of these works received an average of 12 citations. The most active country was found to be the USA, and the journal with the highest number of publications was 'Artificial Intelligence in Medicıne'. 'Machine learning' was found to be the most frequently used and leading theme of the field. It was determined that the themes that shape the field the most are 'pregnancy, preterm birth, covid‐19'. It was determined that the specific themes specific to the field are 'fuzzy logic, ECG'. It was determined that 'segmentation, MRI, ensemble learning' are among the emerging or disappearing themes of the field. The increase in publications between 1983 and 2025 is important in terms of showing the increasing interest in the subject. While machine learning constitutes the main themes of the field, concepts such as 'pregnancy, preterm birth, covid‐19' have emerged as critical foci that increase the social and clinical impact of the field. While original themes such as 'fuzzy logic' and 'ECG' encourage specialisation in the field, new and changing topics such as 'segmentation, MRI, ensemble learning' provide insight into the directions of future research. All of these trends enable the more sensitive and holistic assessment of motor, cognitive and physiological developmental indicators specific to infancy through artificial intelligence methods. Highlights: By analysing the number of publications, citation indexes and collaboration networks of studies on artificial intelligence on infants, the study shows how dynamic and developing the field is. It is considered to be a pioneering study that will guide future studies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|