Related Experiment Video
Updated: Jun 6, 2026
Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
Published on: April 5, 2019
Artificial intelligence in pediatric pain: a systematic review
Ziyang Wang1, Jinjiu Hu1, Jinsong Zeng1
1Department of Nursing, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents' Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders，Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, No.136, 2nd Zhongshan Road, Yu Zhong District, Chongqing, 400010, China.
Insights
Artificial intelligence (AI) enhances pediatric pain assessment and management. Deep learning and multimodal data integration are key for future advancements in AI for pediatric pain.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Pediatric Medicine
Background:
- Pain is a complex symptom in pediatric patients.
- Artificial intelligence (AI) offers potential solutions for pediatric pain management.
- This review examines AI applications in pediatric pain.
Conclusions:
- AI technology is effective in improving pediatric pain detection and assessment.
- Future advancements require integrating deep learning with multimodal data and large clinical datasets.
- Standardized datasets and real-world validation are crucial for precise AI-driven pain management.
Background:
Pain is a challenging, multifaceted symptom reported by most pediatric patients. This systematic review aims to explore the progress and effectiveness of applying artificial intelligence (AI) technology in pediatric pain management.
Methods:
A comprehensive search of PubMed, Embase, Web of Science, Cochrane, Scopus, IEEE Xplore, ACM Library, and ClinicalTrials.gov was conducted. The search combined pain-related terms ("Pain management", "Pain assessment", "Pain measurement", "Pain relief", "Pain control", "Analgesics", "Pediatric pain"), age-related terms ("Children", "pediatrics", "Neonate", "Infant"), and AI core sub-domain terms ("Artificial intelligence", "Machine learning", "Deep learning", "Convolutional neural network", "Support vector machine", "Random forest", "Long short-term memory") using strict Boolean operators (AND/OR/NOT) published up to March 20, 2026. AI models, their validation, and performance were summarized.
Results:
The analysis of 71 studies revealed distinct AI application patterns in pediatric pain management. Fifty-nine studies focused on pain assessment using deep learning (post-2020: 86.7%) and classical machine learning (pre-2015: 83.3%) through facial expression analysis (40.8%), multimodal fusion (25.4%), or physiological signals (9.9%). Most employed observational designs (57.7%) with small (<50 participants, 42.2%) to medium (50-200, 33.8%) samples, reflecting clinical data challenges. Multimodal approaches significantly outperformed unimodal methods (AUC difference: +0.13, p<0.01). The remaining 12 studies (16.9%) explored pain management, primarily using robot-assisted interventions with cognitive-behavioral strategies like guided breathing and gamification during procedural pain. However, a substantial proportion of the included studies (47.5% of assessment studies and 11 of 12 intervention studies) showed high risk of bias.
Conclusion:
This review offers substantiation that AI technology has been employed to enhance the efficiency of pain detection and assessment, thereby assisting healthcare professionals and patients in more adeptly managing acute pain. Future progress in precise pain assessment and management will depend on integrating deep learning with multimodal data and large clinical databases, alongside efforts to establish standardized datasets and validate models in real-world settings.
