SynthesisJournal of pain and symptom management2024
Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.
Synthesis in Journal of pain and symptom management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Machine learning application in the prediction of postoperative delirium among elderly patients: a systematic review and meta-analysis.Langenbeck's archives of surgery · 2026Pooled it
- Prospective Dose-Finding of Esketamine for Suppressing Cervical Dilation Response in Ambulatory Hysteroscopy Under Monitored Anesthesia Care.Drug design, development and therapy · 2025Trial
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Healthcare professionals' perceptions and needs for a Home-based Artificial Intelligence Pain Management System for cancer pain: A qualitative study.International journal of nursing sciences · 2026Article
- Visualization analysis of research progress in precision oncology nursing based on CiteSpace.BMC nursing · 2026Article
- Postoperative arrhythmias and long-term cardiac function in pediatric congenital heart disease after cardiopulmonary bypass: a single-center retrospective cohort study.European journal of pediatrics · 2026Article
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- Implementation of artificial intelligence in palliative and supportive care for people with cancer: A scoping review.Palliative medicine · 2026Article
- Machine Learning Applications for Opioid Use Management in Chronic Cancer Pain: A Systematic Scoping Review.Cureus · 2026Review
- Impact of Artificial Intelligence on the Care of Terminally Ill Patients.Healthcare (Basel, Switzerland) · 2026Review
- Review
- Behavioral assessment of pain in rodents: advances from evoked responses to spontaneous states and multimodal approaches.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Reshaping Precision Cancer Pain Management with Multimodal Artificial Intelligence: A Review on Subtyping and Treatment Response Prediction.Journal of pain research · 2026Review
- Breaking Through Cancer Pain: From Single-Drug Management to Multimodal Analgesia.Pain research & management · 2026Review
- Review
- Rethinking Pain Assessment: Subjective Scales, Biomarkers, and Multimodal Integration.Journal of pain research · 2026Review
- Avenues for integrating photothermal therapy in cancer clinic.Communications chemistry · 2025Review
- A roadmap for artificial intelligence in pain medicine: current status, opportunities, and requirements.Current opinion in anaesthesiology · 2025Review
Corrections and comments
- Update of
Authors and funding
11 authors.
Funding
Abstract
BACKGROUND/
objectivesPain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.
methodsA comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.
resultsForty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).
conclusionImplementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.
Indexed as
Identifiers
What Socratic 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 Socratic graph.