Evidence map›Paper›PMID 39802619›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2025

AI-Driven Management of Type 2 Diabetes in China: Opportunities and Challenges.

Zhifang He, Wenyu Li

Abstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Zhifang HeShanghai Xuhui Area Government of Community Office of Kangjian Xincun Street, Shanghai, People's Republic of China.
Wenyu LiSchool of Marxism, Capital Normal University, Beijing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the aging of China's population and lifestyle changes, the number of patients with type 2 diabetes (T2D) has surged, posing a significant challenge to the public health system. This study explores the application and effectiveness of artificial intelligence (AI) technology in T2D management from a Chinese perspective. AI demonstrates substantial potential in personalized treatment planning, real-time monitoring and early warning, telemedicine, and health management. It not only enhances the precision and convenience of treatment but also aids in preventing and managing complications. Despite challenges in data privacy, technology popularization, standardization, and regulation, AI technology's continuous maturation and expanded application suggest its increasingly pivotal role in T2D management. In the future, through interdepartmental collaboration, policy support, and cultural adaptation, AI is poised to bring revolutionary changes to diabetes management in China and globally.

Indexed as

artificial intelligencedata privacy and securitydiabetes managementpersonalized treatmenttelemedicinetype 2 diabetes

Identifiers

PMID39802619
PMCPMC11718508

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.