ReviewPain reports
Artificial intelligence and machine learning in pain research: a data scientometric analysis.
Review in Pain reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07038434 (Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations), which is not on this map. Cited by 26 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.
Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.Frontiers in oncology · 2024Pooled it
- Integrated quantitative sensory testing and psychological phenotyping dataset for capsaicin-induced neuropathic pain susceptibility.Data in brief · 2026Article
- Neuroligins and Neuropathic Pain: Insights into Synaptic Plasticity and Pain Transmission.Biology · 2026Review
- The complexity of pain in osteoarthritis.Nature reviews. Rheumatology · 2026Review
- Refining multiple artificial intelligence strategies for automatic pain assessment investigations (RUGGI Study): A study protocol.European journal of anaesthesiology and intensive care · 2026Article
- Predicting the course of high-impact chronic pain using machine learning algorithms.The journal of pain · 2026Article
- Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients.Nursing in critical care · 2026Article
- Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data.European journal of pain (London, England) · 2026Article
- Development of a machine learning-based nomogram for predicting chronic postsurgical pain after uniportal video-assisted thoracoscopic surgery.Journal of thoracic disease · 2025Article
- [Creation of Patient Profiles and a Screening Tool for Individuals with Pain].Farmaceuticos comunitarios · 2025Article
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- The need for epistemic humility in AI-assisted pain assessment.Medicine, health care, and philosophy · 2025Article
- Predicting pain and its association with mortality in patients with stroke.BMC neurology · 2025Article
- Biases in Artificial Intelligence Application in Pain Medicine.Journal of pain research · 2025Review
- Disease Phenotypes in Refractory Musculoskeletal Pain Syndromes Identified by Unsupervised Machine Learning.ACR open rheumatology · 2024Article
- Advances and challenges in neuroimaging-based pain biomarkers.Cell reports. Medicine · 2024Review
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism.Clinical and translational science · 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
The collection of increasing amounts of data in health care has become relevant for pain therapy and research. This poses problems for analyses with classical approaches, which is why artificial intelligence (AI) and machine learning (ML) methods are being included into pain research. The current literature on AI and ML in the context of pain research was automatically searched and manually curated. Common machine learning methods and pain settings covered were evaluated. Further focus was on the origin of the publication and technical details, such as the included sample sizes of the studies analyzed with ML. Machine learning was identified in 475 publications from 18 countries, with 79% of the studies published since 2019. Most addressed pain conditions included low back pain, musculoskeletal disorders, osteoarthritis, neuropathic pain, and inflammatory pain. Most used ML algorithms included random forests and support vector machines; however, deep learning was used when medical images were involved in the diagnosis of painful conditions. Cohort sizes ranged from 11 to 2,164,872, with a mode at n = 100; however, deep learning required larger data sets often only available from medical images. Artificial intelligence and ML, in particular, are increasingly being applied to pain-related data. This report presents application examples and highlights advantages and limitations, such as the ability to process complex data, sometimes, but not always, at the cost of big data requirements or black-box decisions.
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.