Artificial Intelligence and Child Abuse and Neglect: A Systematic Review
All societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Children |
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| Online Access: | https://www.mdpi.com/2227-9067/10/10/1659 |
| _version_ | 1827721354086973440 |
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| author | Francesco Lupariello Luca Sussetto Sara Di Trani Giancarlo Di Vella |
| author_facet | Francesco Lupariello Luca Sussetto Sara Di Trani Giancarlo Di Vella |
| author_sort | Francesco Lupariello |
| collection | DOAJ |
| description | All societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed the types of AI models that have been developed/validated. Furthermore, no indications about the risk of bias in these studies are available. For these reasons, the authors conducted a systematic review of the PubMed database to answer the following questions: “what is the state of the art about the development and/or validation of AI predictive models useful to contrast child abuse and neglect phenomenon?”; “which is the risk of bias of the included articles?”. The inclusion criteria were: articles written in English and dated from January 1985 to 31 March 2023; publications that used a medical and/or protective service dataset to develop and/or validate AI prediction models. The reviewers screened 413 articles. Among them, seven papers were included. Their analysis showed that: the types of input data were heterogeneous; artificial neural networks, convolutional neural networks, and natural language processing were used; the datasets had a median size of 2600 cases; the risk of bias was high for all studies. The results of the review pointed out that the implementation of AI in the child abuse and neglect field lagged compared to other medical fields. Furthermore, the evaluation of the risk of bias suggested that future studies should provide an appropriate choice of sample size, validation, and management of overfitting, optimism, and missing data. |
| first_indexed | 2024-03-10T21:20:24Z |
| format | Article |
| id | doaj.art-297b326df7974e07ad8c1986e5b94685 |
| institution | Directory Open Access Journal |
| issn | 2227-9067 |
| language | English |
| last_indexed | 2024-03-10T21:20:24Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj.art-297b326df7974e07ad8c1986e5b946852023-11-19T16:05:14ZengMDPI AGChildren2227-90672023-10-011010165910.3390/children10101659Artificial Intelligence and Child Abuse and Neglect: A Systematic ReviewFrancesco Lupariello0Luca Sussetto1Sara Di Trani2Giancarlo Di Vella3Dipartimento di Scienze della Sanità Pubblica e Pediatriche, Sezione di Medicina Legale, Università degli Studi di Torino, 10126 Torino, ItalyDipartimento di Scienze della Sanità Pubblica e Pediatriche, Sezione di Medicina Legale, Università degli Studi di Torino, 10126 Torino, ItalyDipartimento di Scienze della Sanità Pubblica e Pediatriche, Sezione di Medicina Legale, Università degli Studi di Torino, 10126 Torino, ItalyDipartimento di Scienze della Sanità Pubblica e Pediatriche, Sezione di Medicina Legale, Università degli Studi di Torino, 10126 Torino, ItalyAll societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed the types of AI models that have been developed/validated. Furthermore, no indications about the risk of bias in these studies are available. For these reasons, the authors conducted a systematic review of the PubMed database to answer the following questions: “what is the state of the art about the development and/or validation of AI predictive models useful to contrast child abuse and neglect phenomenon?”; “which is the risk of bias of the included articles?”. The inclusion criteria were: articles written in English and dated from January 1985 to 31 March 2023; publications that used a medical and/or protective service dataset to develop and/or validate AI prediction models. The reviewers screened 413 articles. Among them, seven papers were included. Their analysis showed that: the types of input data were heterogeneous; artificial neural networks, convolutional neural networks, and natural language processing were used; the datasets had a median size of 2600 cases; the risk of bias was high for all studies. The results of the review pointed out that the implementation of AI in the child abuse and neglect field lagged compared to other medical fields. Furthermore, the evaluation of the risk of bias suggested that future studies should provide an appropriate choice of sample size, validation, and management of overfitting, optimism, and missing data.https://www.mdpi.com/2227-9067/10/10/1659artificial intelligencechild abusemachine learningdeep learningneglectchild sexual abuse |
| spellingShingle | Francesco Lupariello Luca Sussetto Sara Di Trani Giancarlo Di Vella Artificial Intelligence and Child Abuse and Neglect: A Systematic Review Children artificial intelligence child abuse machine learning deep learning neglect child sexual abuse |
| title | Artificial Intelligence and Child Abuse and Neglect: A Systematic Review |
| title_full | Artificial Intelligence and Child Abuse and Neglect: A Systematic Review |
| title_fullStr | Artificial Intelligence and Child Abuse and Neglect: A Systematic Review |
| title_full_unstemmed | Artificial Intelligence and Child Abuse and Neglect: A Systematic Review |
| title_short | Artificial Intelligence and Child Abuse and Neglect: A Systematic Review |
| title_sort | artificial intelligence and child abuse and neglect a systematic review |
| topic | artificial intelligence child abuse machine learning deep learning neglect child sexual abuse |
| url | https://www.mdpi.com/2227-9067/10/10/1659 |
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