Can artificial intelligence achieve human-level performance? A pilot study of childhood sexual abuse detection in self-figure drawings

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 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.

Pediatrics in Artificial Intelligence Era: A Systematic Review on Challenges, Opportunities, and Explainability

BACKGROUND

The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, the practical application of AI still faces a number of key challenges. Consequently, there is a requirement for a concise overview of the roles of AI across the multiple domains of pediatric medicine, which the current study seeks to address.

AIM

To systematically assess the challenges, opportunities, and explainability of AI in pediatric medicine.

METHODOLOGY

A systematic search was carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, and grey literature using search terms related to machine learning (ML) and AI for the years 2016 to 2022 in the English language. A total of 210 articles were retrieved that were screened with PRISMA for abstract, year, language, context, and proximal relevance to research aims. A thematic analysis was carried out to extract findings from the included studies.

RESULTS

Twenty articles were selected for data abstraction and analysis, with three consistent themes emerging from these articles. In particular, eleven articles address the current state-of-the-art application of AI in diagnosing and predicting health conditions such as behavioral and mental health, cancer, syndromic and metabolic diseases. Five articles highlight the specific challenges of AI deployment in pediatric medicines: data security, handling, authentication, and validation. Four articles set out future opportunities for AI to be adapted: the incorporation of Big Data, cloud computing, precision medicine, and clinical decision support systems. These studies collectively critically evaluate the potential of AI in overcoming current barriers to adoption.

CONCLUSION

AI is proving disruptive within pediatric medicine and is presently associated with challenges, opportunities, and the need for explainability. AI should be viewed as a tool to enhance and support clinical decision-making rather than a substitute for human judgement and expertise. Future research should consequently focus on obtaining comprehensive data to ensure the generalizability of research findings.

Children's drawings as a projective tool to explore and prevent experiences of mistreatment and/or sexual abuse

Reality shows us that situations of mistreatment and sexual abuse in childhood are still seldom visible, despite their high prevalence around the world. It is essential to detect and address them, especially among children in situations of dire risk or neglect. The purpose of this study is to determine if graphic emotional indicators are expressed in the drawings of the projective Draw-a-Person (D.A.P) test, made by children in situations of dire risk or neglect. The sample is made up of 34 children, between the ages of 5 and 11 (17 girls and 17 boys), attended by Specialised Child and Adolescent Care Services of the Barcelona Town Hall (Spain). The drawings were coded quantitatively. The results indicated that most of the drawings show a frequency of graphic emotional indicators, as well as graphic indicators common to experiences of mistreatment and/or abuse, which confirm the existence of emotional problems. However, no significant differences based on gender and age were found, except for one indicator of sexual abuse (body omitted/distorted), which is significantly more common in the boys. Results also revealed that the drawings of human figure enable the children to express their experiences of traumatic situations which are difficult to verbalize. These findings have important implications for professionals, as the use of this projective technique can help to early identification and design treatment strategies in situations of mistreatment and/or abuse in children and their families.

Use of Self-Figure Drawing as an Assessment Tool for Child Abuse: Differentiating between Sexual, Physical, and Emotional Abuse

Child abuse is a worldwide phenomenon with adverse short- and long-term mental and physical negative consequences, with a huge gap between the prevalence of child abuse and disclosure rates. The study aimed to examine and validate the self-figure drawing as an assessment tool to differentiate between three forms of child abuse, i.e., child sexual abuse (CSA), child physical abuse (CPA), and child emotional abuse (CEA). Following the ethical approval, 1707 Thai children (13–18 years old) from the general population (schools) were asked to complete a self-report anonymous questionnaire consisting of four measures (Demographics, Childhood Trauma Questionnaire (CTQ), The Medical Somatic Dissociation Questionnaire (MSDQ), and The Disclosure of Trauma Questionnaire (DTQ)). After completion, they were asked to draw themselves. There was a significantly positive link between the reluctance to disclose and the experience of abuse, indicating that the more severe the abuse the higher the reluctance to disclose. The findings broaden the knowledge of movement and symbols as representations of inner personal conflictual material. Additionally, it substantiates self-figure drawing as an assessment tool and assists practitioners in early child abuse detection.