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Analgesia and Pain Management01:25

Analgesia and Pain Management

Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...

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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.

BMC Medical Informatics and Decision Making
|June 4, 2026
PubMed
Summary

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.

Keywords:
Artificial intelligenceDeep learningMachine learningPainPediatrics

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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.

Purpose of the Study:

  • To explore the progress of AI in pediatric pain management.
  • To evaluate the effectiveness of AI technologies in pediatric pain assessment and control.
  • To identify current trends and future directions for AI in this field.

Main Methods:

  • A systematic literature search was performed across major databases (PubMed, Embase, Web of Science, etc.).
  • Keywords included pain management terms, pediatric age groups, and AI sub-domains (machine learning, deep learning).
  • Studies were analyzed for AI model types, validation, performance, and risk of bias.

Main Results:

  • 71 studies showed AI primarily used for pain assessment (59 studies) via facial analysis and physiological signals, with deep learning dominating recent research.
  • Multimodal AI approaches demonstrated superior performance (AUC difference: +0.13, p<0.01) compared to unimodal methods.
  • 12 studies explored AI in pain management, mainly through robot-assisted interventions, but many studies had a high risk of bias.

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.