ArticleJournal of the American Medical Informatics Association : JAMIA2023
Using artificial intelligence to improve pain assessment and pain management: a scoping review.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed, 2 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta-Analysis.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2026Pooled it
- Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Interactive AI Routines for Pain Symptoms and Loneliness in Older Adults.Pain management nursing : official journal of the American Society of Pain Management Nurses · 2025Trial
- Extraction of Pain Severity and Functional Interference From Clinical Narratives Using Domain-Informed Large Language Models: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Is this neonate feeling pain? Leveraging clinical knowledge towards high-precision Large Language Model-based neonatal pain assessment.Pediatric research · 2026Article
- Artificial intelligence in geriatric healthcare: a scoping review.BMC geriatrics · 2026Article
- Emerging technologies in interventional pain management: a scoping review on current innovations and future directions.BMC anesthesiology · 2026Article
- Pain in Alzheimer's Disease: Disrupted Multilevel Integration of Nociception, Affective Processing and Clinical Expression Across Clinical and Preclinical Evidence.Life (Basel, Switzerland) · 2026Review
- Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients.Nursing in critical care · 2026Article
- AI-powered nursing handoffs: Introducing and evaluating the patient report template.Learning health systems · 2026Article
- Qualitative validation of TnED©, an electronic instrument capturing pain dimensions in patients with trigeminal neuralgia.Journal of oral & facial pain and headache · 2026Article
- The impact of regional block presence on large language model-based postoperative analgesia recommendations in abdominal surgery: a comparative study using real-world patient data.BMC anesthesiology · 2026Observational
- Digital Pain Assessment: Patient and Family Perspectives.Nursing reports (Pavia, Italy) · 2026Article
- Evaluating AI-powered learning assistants in engineering higher education with implications for student engagement, ethics, and policy.Scientific reports · 2026Article
- Artificial Intelligence, Informatics, and Pain in Aging.Clinics in geriatric medicine · 2026Review
- Impact of internet-based multidisciplinary management model on continuity of care intervention for postoperative outcomes in patients with mixed hemorrhoids: a retrospective cohort study.Frontiers in medicine · 2026Article
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Rethinking Pain Assessment: Subjective Scales, Biomarkers, and Multimodal Integration.Journal of pain research · 2026Review
- Nurses' Lived Experiences of Assessing Pain in Hospitalized Children in Indonesia: A Qualitative Phenomenological Study.Journal of multidisciplinary healthcare · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
contextOver 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.
objectivesThis review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.
methodsThe electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.
resultsThis review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.
conclusionsFindings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.
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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.