ReviewPain and therapy2021
Machine Learning in Pain Medicine: An Up-To-Date Systematic Review.
Review in Pain and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Artificial Intelligence in Neuropathic Pain: From Mechanisms to Neuromodulation and Regenerative Strategies.Current pain and headache reports · 2026Review
- Leveraging artificial intelligence to optimize neuromodulation in the treatment of patients with chronic pain.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Pain assessment and determination methods with wearable sensors: a scoping review.Medical & biological engineering & computing · 2026Article
- Machine learning methods on BioVid heat pain database for pain intensity estimation.Scientific reports · 2025Article
- Machine learning classification of EEG responses to pain-related vs non-pain-related stimulus in preterm infants.Pain reports · 2025Article
- Epigenetic Dysregulation in Neurodegenerative Disease: Implications for Neuropathology and Therapy.Cureus · 2025Review
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Expert consensus on feasibility and application of automatic pain assessment in routine clinical use.Journal of anesthesia, analgesia and critical care · 2025Review
- Machine learning research methods to predict postoperative pain and opioid use: a narrative review.Regional anesthesia and pain medicine · 2025Review
- Microneedles as a Promising Technology for Disease Monitoring and Drug Delivery: A Review.ACS materials Au · 2025Review
- Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee.Pain practice : the official journal of World Institute of Pain · 2025Article
- Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Review
- Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review.Pain and therapy · 2024Review
- Prediction of the Need for Anticonvulsants in the Management of Orofacial Neuropathic Pain Using Machine Learning.Cureus · 2024Article
- Pain Research and Treatment: Policy Implications.Policy insights from the behavioral and brain sciences · 2024Article
- Transforming medicine: artificial intelligence integration in the peripheral nervous system.Frontiers in neurology · 2024Review
- Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.Cyborg and bionic systems (Washington, D.C.) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain.
methodsA systematic review of the current literature was conducted using the PubMed database library.
resultsTwenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed.
conclusionML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.
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.