ArticleJournal of medical Internet research2023
The Clinical Suitability of an Artificial Intelligence-Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Clinical rating scales for assessing pain in newborn infants.The Cochrane database of systematic reviews · 2025Pooled it
- Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- 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
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Machine learning classification of EEG responses to pain-related vs non-pain-related stimulus in preterm infants.Pain reports · 2025Article
- Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.Clinical and experimental pediatrics · 2025Article
- Identification of technology-based models and efficacy of digital-based pain facial expression assessment tools among children: a systematic review.BMC nursing · 2025Article
- Personalizing ICU liberation for critically Ill children: shaping the future of the ABCDEF bundle.Current opinion in pediatrics · 2025Review
- Comparison between AI and human expert performance in acute pain assessment in sheep.Scientific reports · 2025Article
- Caregivers' role regarding managing postoperative pain of hospitalised children (0-3 years).Health SA = SA Gesondheid · 2025Article
- Parental Assessment of Postsurgical Pain in Infants at Home Using Artificial Intelligence-Enabled and Observer-Based Tools: Construct Validity and Clinical Utility Evaluation Study.JMIR pediatrics and parenting · 2024Article
- Article
- Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.Journal of personalized medicine · 2024Review
- Availability of Observational Pain Assessment Tools in Hospitalized Patients with Osteoporotic Vertebral Fractures.Medicina (Kaunas, Lithuania) · 2024Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInfants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its management. The introduction of mobile health-based (mHealth) pain assessment tools could address current challenges and is an area requiring further research.
objectiveThe purpose of this study is to evaluate the accuracy and feasibility aspects of PainChek Infant and, therefore, assess its applicability in the intended setting.
methodsBy observing infants just before, during, and after immunization, we evaluated the accuracy and precision at different cutoff scores of PainChek Infant, which is a point-of-care mHealth-based solution that uses artificial intelligence to detect pain and intensity based solely on facial expression. We used receiver operator characteristic analysis to assess interpretability and establish a cutoff score. Clinician comprehensibility was evaluated using a standardized questionnaire. Other feasibility aspects were evaluated based on comparison with currently available observational pain assessment tools for use in infants with procedural pain.
resultsBoth PainChek Infant Standard and Adaptive modes demonstrated high accuracy (area under the curve 0.964 and 0.966, respectively). At a cutoff score of ≥2, accuracy and precision were 0.908 and 0.912 for Standard and 0.912 and 0.897 for Adaptive modes, respectively. Currently available data allowed evaluation of 16 of the 17 feasibility aspects, with only the cost of the outcome measurement instrument unable to be evaluated since it is yet to be determined. PainChek Infant performed well across feasibility aspects, including interpretability (cutoff score defined), ease of administration, completion time (3 seconds), and clinician comprehensibility.
conclusionsThis work provides information on the feasibility of using PainChek Infant in clinical practice for procedural pain assessment and monitoring, and demonstrates the accuracy and precision of the tool at the defined cutoff score.
Indexed as
Identifiers
What OpenQuestion holds
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.