ReviewJMIR AI2025
Survey on Pain Detection Using Machine Learning Models: Narrative Review.
Review in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Automated Vision-Based Sensing of Paediatric Pain Using 2D-Facial Landmark Trajectories Derived from 3D Geometric Normalisation and a Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) Network.Sensors (Basel, Switzerland) · 2026Article
- Decentralized Tele-Rehabilitation via Edge AI-Oracle Architecture for Spatiotemporal Pain Assessment.Sensors (Basel, Switzerland) · 2026Article
- Towards Intelligent Pain Monitoring Systems: A Survey of Recent Technologies and Methods.Sensors (Basel, Switzerland) · 2026Review
- Review
- The Fifth Edition of the Automated Assessment of Pain (AAP 2025).Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPain, a leading reason people seek medical care, has become a social issue. Automated pain assessment has seen notable advancements over recent decades, addressing a critical need in both clinical and everyday settings.
objectiveThe objective of this survey was to provide a comprehensive overview of pain and its mechanisms, to explore existing research on automated pain recognition modalities, and to identify key challenges and future directions in this field.
methodsA literature review was conducted, analyzing studies focused on various modalities for automated pain recognition. The modalities reviewed include facial expressions, physiological signals, audio cues, and pupil dilation, with a focus on their efficacy and application in pain assessment.
resultsThe survey found that each modality offers unique contributions to automated pain recognition, with facial expressions and physiological signals showing particular promise. However, the reliability and accuracy of these modalities vary, often depending on factors such as individual variability and environmental conditions.
conclusionsWhile automated pain recognition has progressed considerably, challenges remain in achieving consistent accuracy across diverse populations and contexts. Future research directions are suggested to address these challenges, enhancing the reliability and applicability of automated pain assessment in clinical practice.
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.