Evidence map›Paper›PMID 42553127›Full record

ReviewFrontiers in medicine2026

Status, challenges, and prospects of artificial intelligence application in gout diagnosis and treatment, drug research and development, and disease monitoring.

Jing Ma, Jing Zhao, Xinru Liu, Jin Yan, Yunxia Hou, Chunlei Li, Dafu Man, Cheng Wang, Hongbin Li, Yong Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in medicine, 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

10 authors.

Jing Ma *Department of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Jing Zhao *Department of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Xinru LiuDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Jin YanDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Yunxia HouDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Chunlei LiDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Dafu ManDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Cheng WangDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Hongbin LiDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Yong WangDepartment of Rheumatology and Immunology, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gout is a chronic inflammatory disease caused by the deposition of monosodium urate crystals. Its global prevalence is rising, with an observable trend toward an earlier age at onset. Clinical management faces several persistent challenges, including delayed diagnosis, inaccurate disease assessment, a lack of individualized treatment strategies, challenges in long-term follow-up, and suboptimal patient adherence. In recent years, artificial intelligence (AI) has demonstrated significant potential across the gout care continuum. Leveraging its robust capabilities in data processing, pattern recognition, and predictive analytics, AI offers novel approaches to address these clinical challenges. This review systematically collates published investigations concerning AI in the diagnosis and clinical management of gout, delineates the current developmental stage of relevant work, and outlines viable directions for subsequent research in this domain.

Indexed as

application statusartificial intelligencechallenges and prospectsdiagnosis and treatmentgout

Identifiers

PMID42553127
PMCPMC13433292

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.