Reprinted with permission of EFIC EJP: 29 (2025): e4748: Moving towards the use of artificial intelligencein pain management
Bibliographic record
Abstract
Background and objective: While the development of artificial intelligence(AI) technologies in medicine has beensignificant, their application to acute and chronic pain management has not been well characterized. This systematicre-view aims to provide an overview of the current state of AI in acute and chronic pain management. Databases and data treatment: This review was registered with PROSPERO(ID# CRD42022307017), the internationalregistry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management. Results: From the 17,601 records identified in the initial search, 197 were included in this review. Identified applicationsof AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain,forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for proceduralinterventions and self-management tools. Multiple domains of AI were used including machine learning, computervision, fuzzy logic, natural language processing and expert systems. Conclusions: There is growing literature regarding applications of AI for pain management, and their clinical use holdspotential for improving patient out-comes. However, multiple barriers to their clinical integration remain including lackvalidation of such applications in diverse patient populations, missing infra-structure to support these tools andlimited provider understanding of AI. Significance: This review characterizes current applications of AI for pain management and discusses barriersto their clinical integration. Our findings support continuing efforts directed towards establishing comprehensivesystems that integrate AI throughout the patient care continuum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.750 | 0.401 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".