Evidence map›Paper›PMID 41930234›Full record

ReviewFrontiers in pain research (Lausanne, Switzerland)2026

AI applications across the cancer pain care Continuum: applications and future directions.

Danlin Zhu, Han Zhenkai

Abstract readReview
In one paragraph

Review in Frontiers in pain research (Lausanne, Switzerland), 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.

Danlin ZhuDepartment of Pain Management, Shengjing Hospital of China Medical University, Shenyang, China.
Han ZhenkaiDepartment of Pain Management, Shengjing Hospital of China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given the rapid expansion of AI applications across multiple dimensions of cancer pain management, there is an urgent need to synthesize current evidence and clarify future directions to guide clinical and research practice. Cancer pain remains one of the most central and challenging issues in pain medicine. Its pathogenesis is multifaceted, involving tumor invasion, treatment-related injury, and diverse biopsychosocial factors; assessment is difficult, treatment decisions involve numerous variables, and long-term management is resource-intensive. Epidemiological data indicate that approximately half of all patients with cancer experience moderate-to-severe pain during the disease course, with the burden markedly higher in advanced stages. The rapid advancement of AI in medicine has spurred growing interest in its potential contributions to cancer pain assessment, analgesic decision-making, remote follow-up, and interventional planning. This mini-review, organized from a clinical care-pathway perspective, summarizes recent applications of AI in cancer pain assessment, opioid management, remote monitoring, and interventional or longitudinal care. We further discuss methodological and real-world challenges, emphasizing how AI may ultimately contribute to an integrated, longitudinal management framework for cancer pain.

Indexed as

artificial intelligencecancer paindecision supportpain assessmentwearable monitoring

Identifiers

PMID41930234
PMCPMC13039019

What OpenQuestion holds

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LicenceCC BY
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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.