AI-Driven Pain Monitoring Dashboard for Infants: A Pilot Study for NICU and Family Medicine Clinics

Abstract Effective pain assessment in infants aged 0–3 months is a critical challenge in neonatal intensive care units (NICUs) and family medicine clinics, where self-reporting is impossible and current observational tools remain subjective and inconsistent. This paper presents a pilot study proposal for an AI-driven pain monitoring dashboard designed to support clinicians and caregivers in real time. The system integrates multimodal inputs, including facial video, cry audio, and optional physiological signals, processed through a deep learning framework with self-supervised pretraining and few-shot adaptation to overcome limited labeled neonatal data. A clinician-friendly dashboard displays continuous pain scores, highlights salient modality contributions, and issues alerts during high-risk events. The approach ensures transparency and trust by providing explainability through saliency maps and modality-specific visualizations. This pilot will evaluate the feasibility, usability, and clinical impact of the dashboard in NICU and family clinic environments. The proposed system offers a practical and ethically compliant pathway for deploying explainable AI in early-life pain management, with potential to improve care quality and support medical decision-making.