Affective Computing in the Assessment, Management and Care of Children

The landscape of pediatric healthcare is undergoing a paradigm shift from disease-centric models toward holistic, integrated intervention and care that prioritizes the emotional well-being of children. Neonates and children, particularly those with neurodevelopmental disorders or in critical care settings, often face significant barriers in verbalizing pain and psychological distress. Affective computing—an interdisciplinary field focused on systems that recognize, interpret, and respond to human emotions—offers a vital bridge to overcome these communication gaps. Recent breakthroughs in multimodal deep learning, mobile wearable sensing, and foundation models have enabled the objective quantification of affective states through behavioral features and neurophysiological signals based on artificial intelligence (AI) methods. However, translating these laboratory successes into clinical applications requires addressing unique pediatric challenges, including data scarcity, the prevalence of unstructured data loss in real-world clinical scenarios, and the nuanced “masking” behaviors in neurodivergent populations. This Research Topic aims to consolidate emerging evidence on AI-driven emotional intelligence to humanize pediatric diagnosis, treatment, and nursing.Goal The primary objective of this Research Topic is to bridge the divide between advanced computational emotion modeling and clinical pediatric practice. We aim to showcase innovative methodologies that utilize affective computing to enhance diagnostic precision, personalize therapeutic interventions, and alleviate the cognitive burden on pediatric caregivers. By focusing on critical domains such as pediatric pain management, autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), this collection seeks to establish evidence-based AI-assisted frameworks for pediatric clinical scenarios. Furthermore, we seek to evaluate the clinical efficacy of emotionally adaptive technologies—such as responsive electrical stimulation and intelligent robotic platforms—while addressing the ethical imperatives, including transparency, bias mitigation, and data privacy in pediatric AI deployment.Scope and Information for Authors This research topic welcomes original research, systematic reviews, and perspective articles focusing on the intersection of affective computing and pediatric health. Areas of interest include, but are not limited to:● Neonatal/Pediatric Physiology and Behavior Monitoring: Non-invasive and unobtrusive monitoring of physiological and psychological states in newborns and children.● Neurodevelopmental Disorders Diagnosis and Treatment: AI-assisted diagnosis and emotion regulation interventions for ASD, ADHD, and related conditions.● Intelligent Nursing: Conversational AI, ambient sensing, and clinical decision support systems for clinical nursing such as pain assessment and management.● Advanced AI Methodologies: Multimodal fusion of EEG, fNIRS, behavioral characteristics, and biometric signals for “digital psychological signatures”.● Ethics & Translation: Strategies and real-world applications for data standardization, algorithmic fairness, and clinical workflow integration. Topic editors: Huaiyu Zhu, Zhiyong Wang, Jingjing Liu Source: https://www.frontiersin.org/research-topics/80923/affective-computing-in-the-assessment-management-and-care-of-children