Related Experiment Videos

Stakeholder perspectives on AI-assisted pain assessment in pediatrics: A qualitative study of shared needs and

Ziyang Wang1, Jinjiu Hu1, Xianlan Zheng1

  • 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, Chongqing 400010, China.

The Journal of Pain
|August 13, 2026
PubMed

Insights

Artificial intelligence (AI) can improve pediatric pain assessment by providing objective monitoring. However, successful implementation requires integrating AI into workflows and building trust, addressing varied stakeholder needs for clinical support, empowerment, and communication.

Area of Science:

  • Pediatric healthcare technology
  • Artificial intelligence in medicine
  • Pain management innovations

Background:

  • Pediatric pain is often under-assessed and undertreated, posing a significant clinical challenge.
  • Artificial intelligence (AI) integration in healthcare is growing, but its application in pediatric pain management is limited.
  • Understanding stakeholder perceptions is vital for developing effective and ethical AI solutions for pediatric pain assessment.

Purpose of the Study:

  • To explore the needs, implementation priorities, and barriers related to AI-based pain assessment in pediatric care.
  • To gather insights from children, family caregivers, and medical staff on AI adoption in pain management.

Main Methods:

  • A qualitative study involving 35 participants (11 children, 12 family caregivers, 12 medical staff) from September to October 2025.
  • Individual interviews were conducted, guided by the Technology Acceptance Model.
  • Thematic analysis was used to analyze interview data.

Main Results:

  • All stakeholder groups recognized AI's value for objective, continuous pain monitoring.
  • Divergent priorities emerged: medical staff sought clinical decision support, caregivers desired empowerment, and children focused on communication and emotional support.
  • Key barriers included workload and integration challenges; willingness to use AI depended on proven accuracy, workflow compatibility, and safety.

Conclusions:

  • Significant disparities exist in stakeholder priorities for AI in pediatric pain care.
  • AI should augment, not replace, clinical judgment, with successful implementation dependent on seamless workflow integration and trust.
  • Findings provide guidance for translating AI tools into practice to improve pain assessment accuracy and enhance pediatric pain care quality and equity.