On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review
Purpose: Care and attention during the neonatal period are crucial to preventing negative outcomes. The literature presents artificial intelligence models as promising tools to assist healthcare professionals in disease prediction and support clinical decision-making. Methods: This study conducts a...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11048493/ |
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| author | Flavio Leandro de Morais Raysa Carla Leal da Silva Anna Beatriz Silva Estefani Pontes Simao Maria Eduarda Ferro de Mello Stephany Paula da Silva Canejo Katia Maria Mendes Waldemar Brandao Neto Jackson Raniel Florencio da Silva Maicon Herverton Lino Ferreira da Silva Barros Patricia Takako Endo |
| author_facet | Flavio Leandro de Morais Raysa Carla Leal da Silva Anna Beatriz Silva Estefani Pontes Simao Maria Eduarda Ferro de Mello Stephany Paula da Silva Canejo Katia Maria Mendes Waldemar Brandao Neto Jackson Raniel Florencio da Silva Maicon Herverton Lino Ferreira da Silva Barros Patricia Takako Endo |
| author_sort | Flavio Leandro de Morais |
| collection | DOAJ |
| description | Purpose: Care and attention during the neonatal period are crucial to preventing negative outcomes. The literature presents artificial intelligence models as promising tools to assist healthcare professionals in disease prediction and support clinical decision-making. Methods: This study conducts a bibliometric review of the use of artificial intelligence models in predicting neonatal diseases, conditions and mortality. The review analyzed publications from 2014 to 2024. A total of 629 studies were selected after applying selection criteria. Subsequently, analyses of collaboration networks, keyword co-occurrence, citations and cluster analysis were performed. Results: The results show that the United States, China and the United Kingdom lead scientific production and international collaborations. 12 neonatal diseases were identified, with emphasis on “retinopathy of prematurity”, “necrotizing enterocolitis” and “bronchopulmonary dysplasia”; 7 clinical conditions, including “prematurity”, “perinatal asphyxia” and “jaundice”; and 5 neonatal outcomes, mainly “sepsis”, “mortality” and “cerebral palsy.” Cluster analysis revealed that studies predominantly use clinical, laboratory, genetic and imaging data, with Logistic Regression, Random Forest and Convolutional. Conclusion: The study has growing interest in applying artificial intelligence to neonatal care. The models are increasingly used with clinical, laboratory, genetic and imaging data, enabling earlier and more accurate diagnoses. However, the study also underscores important ethical considerations, such as data quality, algorithmic transparency and equitable access to these technologies, particularly in underrepresented regions, with scientific production uneven and limited participation from low- and middle-income countries. |
| format | Article |
| id | doaj-art-a0cb6e8c554d4e23b162f28ce57f1822 |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a0cb6e8c554d4e23b162f28ce57f18222025-07-24T23:01:18ZengIEEEIEEE Access2169-35362025-01-011312229412231410.1109/ACCESS.2025.358250311048493On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric ReviewFlavio Leandro de Morais0https://orcid.org/0000-0001-9218-2523Raysa Carla Leal da Silva1https://orcid.org/0009-0004-8271-4687Anna Beatriz Silva2https://orcid.org/0009-0008-0826-0030Estefani Pontes Simao3https://orcid.org/0009-0000-9020-4581Maria Eduarda Ferro de Mello4https://orcid.org/0000-0002-1763-0071Stephany Paula da Silva Canejo5https://orcid.org/0000-0001-6216-1035Katia Maria Mendes6Waldemar Brandao Neto7https://orcid.org/0000-0003-4786-9961Jackson Raniel Florencio da Silva8https://orcid.org/0000-0002-4355-7410Maicon Herverton Lino Ferreira da Silva Barros9https://orcid.org/0000-0002-0275-3298Patricia Takako Endo10https://orcid.org/0000-0002-9163-5583Programa de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilBacharelado em Sistemas de Informação, Universidade de Pernambuco (UPE), Caruaru, Pernambuco, BrazilPrograma de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilPrograma de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilPrograma de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilPrograma Associado de Pós-Graduação em Enfermagem (PAPGEnf), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilCentro Universitário Integrado de Saúde Amaury de Medeiros (CISAM), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilPrograma Associado de Pós-Graduação em Enfermagem (PAPGEnf), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilBacharelado em Sistemas de Informação, Universidade de Pernambuco (UPE), Caruaru, Pernambuco, BrazilPrograma de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilPrograma de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, BrazilPurpose: Care and attention during the neonatal period are crucial to preventing negative outcomes. The literature presents artificial intelligence models as promising tools to assist healthcare professionals in disease prediction and support clinical decision-making. Methods: This study conducts a bibliometric review of the use of artificial intelligence models in predicting neonatal diseases, conditions and mortality. The review analyzed publications from 2014 to 2024. A total of 629 studies were selected after applying selection criteria. Subsequently, analyses of collaboration networks, keyword co-occurrence, citations and cluster analysis were performed. Results: The results show that the United States, China and the United Kingdom lead scientific production and international collaborations. 12 neonatal diseases were identified, with emphasis on “retinopathy of prematurity”, “necrotizing enterocolitis” and “bronchopulmonary dysplasia”; 7 clinical conditions, including “prematurity”, “perinatal asphyxia” and “jaundice”; and 5 neonatal outcomes, mainly “sepsis”, “mortality” and “cerebral palsy.” Cluster analysis revealed that studies predominantly use clinical, laboratory, genetic and imaging data, with Logistic Regression, Random Forest and Convolutional. Conclusion: The study has growing interest in applying artificial intelligence to neonatal care. The models are increasingly used with clinical, laboratory, genetic and imaging data, enabling earlier and more accurate diagnoses. However, the study also underscores important ethical considerations, such as data quality, algorithmic transparency and equitable access to these technologies, particularly in underrepresented regions, with scientific production uneven and limited participation from low- and middle-income countries.https://ieeexplore.ieee.org/document/11048493/Artificial intelligencebibliometric reviewneonatal healthcare |
| spellingShingle | Flavio Leandro de Morais Raysa Carla Leal da Silva Anna Beatriz Silva Estefani Pontes Simao Maria Eduarda Ferro de Mello Stephany Paula da Silva Canejo Katia Maria Mendes Waldemar Brandao Neto Jackson Raniel Florencio da Silva Maicon Herverton Lino Ferreira da Silva Barros Patricia Takako Endo On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review IEEE Access Artificial intelligence bibliometric review neonatal healthcare |
| title | On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review |
| title_full | On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review |
| title_fullStr | On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review |
| title_full_unstemmed | On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review |
| title_short | On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review |
| title_sort | on usage of artificial intelligence for predicting neonatal diseases conditions and mortality a bibliometric review |
| topic | Artificial intelligence bibliometric review neonatal healthcare |
| url | https://ieeexplore.ieee.org/document/11048493/ |
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