ArticleScientific reports2024
Comparative analysis of artificial intelligence and expert assessments in detecting neonatal procedural pain.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Potential applications of artificial intelligence in pain management: a scoping review.Frontiers in pain research (Lausanne, Switzerland) · 2026Pooled it
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Beyond the face: advancing multimodal AI for neonatal pain assessment.Pediatric research · 2026Article
- Evaluation of Pain in Infants of the First 100 Days of Age With Artificial Intelligence Techniques.Journal of evaluation in clinical practice · 2026Article
- Artificial Intelligence-Based Video Analysis for Assessing Sucking Behavior in Preterm Infants: A Feasibility Study.Children (Basel, Switzerland) · 2026Article
- Review
- Feasibility and Effectiveness of a Low-Code AI Platform for Developing a Neonatal Multimodal Pain Classification Model.Journal of multidisciplinary healthcare · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessing pain in newborns in the NICU is crucial due to their frequent exposure to painful stimuli, yet it's challenging due to the subjective nature of current methods. This study aimed to evaluate the effectiveness of an AI system designed for automatic facial recognition by comparing its performance with the expert opinion of health care provider. This is a secondary analysis from an eye-tracking study, assessing neonatal pain evaluations by healthcare professionals. The performance of AI software, FaceReader 9, was compared to experts' evaluations using a visual-analog scale, focusing on identifying specific facial action units associated with different pain levels. The study found significant differences in AI-generated metrics-arousal and valence-across three stimulus types: non-noxious thermal, short-noxious, and prolonged-noxious, with p-values below 0.001. A strong correlation (r = 0.84, p ≤ .001) was observed between AI metrics and expert ratings. Eleven facial action units were identified as relevant to describe neonatal pain. The findings highlight the AI system's potential in accurately detecting and analyzing newborn facial expressions in response to varying pain intensities, demonstrating a significant correlation with healthcare professionals' assessments. This suggests that AI technology could enhance objective pain assessment in neonates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.