Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

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.

Use of technology in evidence-based programs for child maltreatment and its impact on parent and child outcomes

Introduction

Technology has been used in evidence-based child maltreatment (CM) programs for over a decade. Although advancements have been made, the extent of the application of technology in these programs, and its influence on parental and child outcomes, remains unclear within the context of changes that emerged because of the COVID-19 pandemic. This scoping review provides a contextualized overview and summary of the use of technology in evidence-based parenting and child programs serving families impacted by child maltreatment and the effects of technology-enhanced programs on target outcomes.

Materials and methods

Using Arksey and O'Malley's methodological framework, we searched seven databases to identify peer-reviewed and grey literature published in English from 2000 to 2023 on evidence-based programs, according to the California Evidence-Based Clearinghouse (CEBC), that included technological supports for two populations: at-risk parents for child maltreatment prevention, and children and youth 0-18 years exposed to child maltreatment. All study designs were included.

Results

Eight evidence-based parenting programs and one evidence-based child trauma program were identified as using technology across a total of 25 peer-reviewed articles and 2 peer-reviewed abstracts meeting inclusion criteria (n = 19 on parent-level programs; n = 8 on child-level programs). Four studies were published in the context of COVID-19. Two main uses of technology emerged: (1) remote programmatic delivery (i.e., delivering all or part of the program virtually using technology) and (2) programmatic enhancement (i.e., augmenting program content with technology). Improvements across parenting and child mental health and behavioral outcomes were generally observed.

Discussion

Technology use in evidence-based child maltreatment programs is not new; however, the small sample since the start of the COVID-19 pandemic in this review that met inclusion criteria highlight the dearth of research published on the topic. Findings also suggest the need for the inclusion of implementation outcomes related to adoption and engagement, which could inform equitable dissemination and implementation of these programs. Additional considerations for research and practice are discussed.

Harnessing Machine Learning in Tackling Domestic Violence-An Integrative Review

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research.

METHODS

We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria.

RESULTS

Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed.

CONCLUSIONS

Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Understanding Alcohol Use Discourse and Stigma Patterns in Perinatal Care on Twitter

(1) Background: perinatal alcohol use generates a variety of health risks. Social media platforms discuss fetal alcohol spectrum disorder (FASD) and other widespread outcomes, providing personalized user-generated content about the perceptions and behaviors related to alcohol use during pregnancy. Data collected from Twitter underscores various narrative structures and sentiments in tweets that reflect large-scale discourses and foster societal stigmas; (2) Methods: We extracted alcohol-related tweets from May 2019 to October 2021 using an official Twitter search API based on a set of keywords provided by our clinical team. Our exploratory study utilized thematic content analysis and inductive qualitative coding methods to analyze user content. Iterative line-by-line coding categorized dynamic descriptive themes from a random sample of 500 tweets; (3) Results: qualitative methods from content analysis revealed underlying patterns among inter-user engagements, outlining individual, interpersonal and population-level stigmas about perinatal alcohol use and negative sentiment towards drinking mothers. As a result, the overall silence surrounding personal experiences with alcohol use during pregnancy suggests an unwillingness and sense of reluctancy from pregnant adults to leverage the platform for support and assistance due to societal stigmas; (4) Conclusions: identifying these discursive factors will facilitate more effective public health programs that take into account specific challenges related to social media networks and develop prevention strategies to help Twitter users struggling with perinatal alcohol use.

Artificial Intelligence Implementation to Counteract Cybercrimes Against Children in Pakistan

Increased internet usage also enhances cyberattacks, particularly in a developing country like Pakistan. These cybercrimes are common against children, demanding the implementation of automated systems and models to detect and counteract these crimes. By keeping in view the growing importance of AI-enabled cybersecurity systems, this article provides insights regarding implementing the relevant systems and models to counteract cybercrimes against children in Pakistan. The researchers reviewed current studies witnessing the performance, reliability, and results of AI implementation in different countries to relate their possible potential further to detect, takedown, and trace online violence against children effectively. Findings showed that AI-enabled software, i.e., Spotlight, SomeBuddy, Google AI Tool, etc., and models such as DAPHNE, iCOP toolkit, and PrevBOT can identify and takedown any indecent activity against children. Besides, detecting the perpetrators’ URLs, domains, and personal emails can further help the children resume internet usage in a healthy online environment. Thus, it is concluded that the internet technology is also creating vectors for abuse and exploitation against children. Harnessing powerful technology, i.e., AI, to analyze and manage the data can also enrich investigative functions. Towards this, local government and law enforcement agencies should resort to suggested tools that may identify even keywords and images. Further, the researchers have provided policy recommendations and discussed the limitations accordingly.

Technology-Based Mental Health Interventions for Domestic Violence Victims Amid COVID-19

INTRODUCTION

Domestic violence is a threat to human dignity and public health. Mounting evidence shows that domestic violence erodes personal and public health, spawning issues such as lifelong mental health challenges. To further compound the situation, COVID-19 and societies' poor response to the pandemic have not only worsened the domestic violence crisis but also disrupted mental health services for domestic violence victims. While technology-based health solutions can overcome physical constraints posed by the pandemic and offer timely support to address domestic violence victims' mental health issues, there is a dearth of research in the literature. To bridge the research gap, in this study, we aim to examine technology-based mental health solutions for domestic violence victims amid COVID-19.

METHODS

A literature review was conducted to examine solutions that domestic violence victims can utilize to safeguard and improve their mental health amid COVID-19. Databases including PubMed, PsycINFO, and Scopus were utilized for the literature search. The search was focused on four themes: domestic violence, mental health, technology-based interventions, and COVID-19. A reverse search of pertinent references was conducted in Google Scholar. The social ecological model was utilized to systematically structure the review findings.

RESULTS

The findings show that a wide array of technology-based solutions has been proposed to address mental health challenges faced by domestic violence victims amid COVID-19. However, none of these proposals is based on empirical evidence amid COVID-19. In terms of social and ecological levels of influence, most of the interventions were developed on the individual level, as opposed to the community level or social level, effectively placing the healthcare responsibility on the victims rather than government and health officials. Furthermore, most of the articles failed to address risks associated with utilizing technology-based interventions (e.g., privacy issues) or navigating the online environment (e.g., cyberstalking).

CONCLUSION

Overall, our findings highlight the need for greater research endeavors on the research topic. Although technology-based interventions have great potential in resolving domestic violence victims' mental health issues, risks associated with these health solutions should be comprehensively acknowledged and addressed.

Chronic high risk of intimate partner violence against women in disadvantaged neighborhoods: An eight-year space-time analysis

We conducted a small-area ecological longitudinal study to analyze neighborhood contextual influences on the spatio-temporal variations in intimate partner violence against women (IPVAW) risk in a southern European city over an eight-year period. We used geocoded data of IPVAW cases with associated protection orders (n = 5867) in the city of Valencia, Spain (2011-2018). The city's 552 census block groups were used as the neighborhood units. Neighborhood-level covariates were: income, education, immigrant concentration, residential instability, alcohol outlet density, and criminality. We used a Bayesian autoregressive approach to spatio-temporal disease mapping. Neighborhoods with low levels of income and education and high levels of residential mobility and criminality had higher relative risk of IPVAW. Spatial patterns of high risk of IPVAW persisted over time during the eight-year period analyzed. Areas of stable low risk and with increasing or decreasing risk were also identified. Our findings link neighborhood disadvantage to the existence and persistence over time of spatial inequalities in IPVAW risk, showing that high risk of IPVAW can become chronic in disadvantaged neighborhoods. Our analytic approach provides specific risk estimates at the small-area level that are informative for intervention purposes, and can be useful to assess the effectiveness of prevention efforts in reducing IPVAW.