Evidence map›Paper›PMID 32617331›Full record

ReviewAnnals of translational medicine2020

Using artificial intelligence to improve medical services in China.

Ruiyang Li, Yahan Yang, Shaolong Wu, Kai Huang, Weirong Chen, Yizhi Liu, Haotian Lin

Abstract readReview
In one paragraph

Review in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. PulmoClass-3DAtt: A Self-Attention Network for Classification of COPD, PRISm and Normal.International journal of chronic obstructive pulmonary disease · 2026
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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

7 authors.

Ruiyang LiState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Yahan YangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Shaolong WuSchool of Public Health, Sun Yat-sen University, Guangzhou, China.
Kai HuangSchool of Data and Computer Science, Sun Yet-sen University, Guangzhou, China.
Weirong ChenState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Yizhi LiuState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Haotian LinState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is one hotspot of research in the field of modern medical technology. Medical AI has been applied to various areas and has two main branches including virtual and physical. Recently, Chinese State Council issued a guideline on developing AI and indicated that the widespread application of AI will improve the level of precision in medical services and achieve the intelligent medical care. Medical resources, especially the high-quality resources, are deficient across the entire health service system in China. AI technologies, such that virtual AI and telemedical technology, are expected to overcome the current limitations of the distribution of medical resources and relieve the pressure associated with obtaining high-quality health care.

Indexed as

Artificial intelligence (AI)Chinahealth caremedical resource

Identifiers

PMID32617331
PMCPMC7327308

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.