ReviewNPJ digital medicine2023
A systematic review of neurophysiological sensing for the assessment of acute pain.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Effect of multimedia education on anxiety and pain in patients undergoing laparoscopic cholecystectomy: a Solomon four-group randomized controlled trial.Scientific reports · 2025Trial
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Localization of pain in patients with probable bruxism: a cross-sectional pain-drawing and heat-map analysis.BMC oral health · 2026Article
- Real-Time Pain Assessment from Electrodermal Activity Using Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Connectivity-Based Pain Recognition from fNIRS: Parsimonious Subject-Independent Classification.Sensors (Basel, Switzerland) · 2026Article
- Evaluating a Wearable-Based Pain Monitoring System in Palliative Cancer Care: Usability and Feasibility Study.JMIR formative research · 2026Observational
- Pain assessment using physiological responses/markers in different types of pain: a scoping review.NPJ digital medicine · 2026Article
- Physiological correlates of pain in preterm infants: evidence from a meta-analytic approach.Frontiers in pediatrics · 2026Review
- Pain assessment and determination methods with wearable sensors: a scoping review.Medical & biological engineering & computing · 2026Article
- AI-guided digital intervention with physiological monitoring reduces intrusive memories after experimental trauma.NPJ digital medicine · 2025Article
- A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion.Scientific reports · 2025Article
- Classifying social and physical pain from multimodal physiological signals using machine learning.Scientific reports · 2025Article
- The story of pain in people with dementia: a rationale for digital measures.BMC medicine · 2025Article
- Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.Sensors (Basel, Switzerland) · 2025Article
- Pain Management in Cancer Patients With Artificial Intelligence: Narrative Review.Scientifica · 2025Review
- Pseudo-labeling based adaptations of pain domain classifiers.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Physiological framework for non-invasive detection and objective nociception activity in communicative patients: a pilot case study.Frontiers in physiology · 2025Article
- Evaluating low back pain through electrodermal activity: a systematic review protocol.BMJ open · 2024Article
- Automated Assessment of Pain (AAP) and Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4Pain).International Conference on Affective Computing and Intelligent Interaction Workshops and Demos · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.
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.