Recent research highlights the increasing incidence of pediatric neurodevelopmental disorders (NDDs) such as autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD), tic disorders, and epilepsy across the globe. While the understanding of the etiology and progression of these disorders has markedly improved, significant deficits remain in their early diagnosis and effective management. Conventional diagnostic methods predominantly subjective, often result in delays in identification and intervention, accentuating a critical need for innovative approaches. Of note, emerging advancements in artificial intelligence (AI) offer promising avenues for enhancing diagnostic precision and fostering personalized treatment strategies, though their practical application in clinical settings is still in its early stages.
This Research topic aims to explore the synergistic potential of AI technologies in enhancing the comprehension, diagnosis, and management of pediatric NDDs. We seek to highlight innovative research that demonstrates how AI can be utilized to analyze complex multimodal data, including neuroimaging, genetic profiles, and behavioral assessments, to improve early detection and tailored interventions. Furthermore, we aim to foster discussions on the ethical, practical, and future directional challenges inherent in the adoption of AI in pediatric neurodevelopmental research. By focusing on these critical issues, we hope to catalyze collaborative efforts between clinicians, researchers, and AI experts to translate findings into impactful clinical practices.
We invite submissions that address various facets of the intersection between pediatric neurodevelopmental disorders and artificial intelligence, including but not limited to:
- AI-based Early-screening/Diagnosis/Prognosis-prediction tools: Research demonstrating the efficacy of AI algorithms in diagnosing NDDs from behavioral, genetic, or neuroimaging data.
- Intervention Strategies: Studies investigating the use of AI in developing personalized treatment plans or real-time monitoring systems for children with NDDs.
- Multimodal Data Fusion: Papers exploring methodologies for integrating diverse data types to enhance understanding of NDDs.
- Ethical Implications: Discussions on the ethical considerations surrounding the use of AI in pediatric populations, including data privacy and informed consent.
- Implementation and Accessibility: Insights into the challenges and solutions for implementing AI technologies in clinical settings, particularly in resource-limited environments.
Authors are encouraged to submit original research articles, reviews, and opinion pieces that contribute to the body of knowledge in this critical area. All submissions will undergo rigorous peer review to ensure the highest quality of research.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Clinical Trial
Conceptual Analysis
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
