Accurately measuring pain in children—especially in infants and neonates who cannot self-report—remains one of pediatric medicine's most persistent clinical gaps. A systematic review mapping AI's role in this space offers a structured view of where the technology stands and, critically, where it still falls short in ways that matter for real-world clinical deployment.
Drawing on 71 studies identified across eight major databases through March 2026, the review found that AI applications in pediatric pain cluster primarily around assessment rather than treatment. Deep learning dominated post-2020 research (accounting for 86.7% of recent studies), while classical machine learning such as support vector machines and random forests prevailed in earlier work. Facial expression analysis was the most common input modality, appearing in roughly 41% of studies, followed by multimodal fusion approaches combining facial, physiological, and contextual signals (25.4%), with pure physiological signal analysis comprising under 10%. A key performance finding: multimodal fusion architectures consistently outperformed single-modality models, suggesting that mimicking the multisensory way clinicians assess pain yields measurably better automated detection.
The structural limitations embedded in this literature deserve careful attention. Nearly 76% of studies used sample sizes below 200 participants—with 42% below 50—a threshold that makes robust external validation nearly impossible. The heavy reliance on observational designs further limits causal inference. These are not minor caveats; they reflect a field that remains largely proof-of-concept rather than clinically deployable. The concentration on facial expression analysis also raises practical questions about generalizability across age groups, skin tones, and sedation states. From a longevity and pediatric health standpoint, better pain quantification in early life carries genuine downstream significance—undertreated acute pain in infancy has documented associations with altered pain sensitivity and stress-response trajectories in later development. This review is a useful landscape map but reinforces that AI pediatric pain tools require substantially larger, diverse, prospective validation cohorts before clinical integration is warranted.