Evidence map›Paper›PMID 42803820›Full record

ReviewJournal of anesthesia2026

Applications of generative artificial intelligence in chronic pain: a scoping review.

Yuriko Matsuoka, Masakazu Hamada

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of anesthesia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yuriko Matsuoka *Department of Kampo and Pain Medicine, NHO Osaka Toneyama Medical Center, 5-1-1 Toneyama, Toyonaka, Osaka, 560-8552, Japan. matsuoka.yuriko.nm@mail.hosp.go.jp.ORCID http://orcid.org/0009-0004-2737-1765
Masakazu Hamada *Department of Oral & Maxillofacial Oncology and Surgery, Graduate School of Dentistry, The University of Osaka, 1-8 Yamadaoka, Suita, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic pain is a complex biopsychosocial condition that imposes substantial clinical and socioeconomic burdens worldwide. Although digital health interventions increasingly support chronic pain management, the role of generative artificial intelligence (AI) remains insufficiently characterized. This scoping review mapped applications of generative AI in chronic pain and identified research gaps. PubMed, Scopus, and Web of Science were searched on October 19, 2025, using "chronic pain" combined with five named generative AI systems. Primary research studies published in English that focused on generative AI applications in chronic pain were included. Of 402 records identified, 25 studies met the eligibility criteria. The studies were classified into five areas: patient and family education and self-management support; evaluation of medical information quality, accuracy, and reliability; pain assessment, analysis, and monitoring; clinical and educational support for healthcare professionals; and interpretation of patient-drawn images. Studies demonstrated technical feasibility across several applications, but most evidence was based on model performance, expert ratings, or retrospective data rather than prospective clinical outcomes. Limitations included variable accuracy, readability, AI hallucinations, inconsistent reporting, and limited external validation. Therefore, generative AI may ultimately serve as an adjunct to clinical judgment, but its clinical benefit and safety remain to be established.

Indexed as

Chronic painClinical decision supportGenerative artificial intelligenceLarge language modelsPain medicine

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.