Evidence map›Paper›PMID 32617318›Full record

ArticleAnnals of translational medicine2020

Web-based study on Chinese dermatologists' attitudes towards artificial intelligence.

Changbing Shen, Chengxu Li, Feng Xu, Ziyi Wang, Xue Shen, Jing Gao, Randy Ko, Yan Jing, Xiaofeng Tang, Ruixing Yu and 4 more

Open access · diamondAbstract read
In one paragraph

Article 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 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
1.8field-weighted citation impact, top 13% of its field
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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.

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  12. Attitudes of optometrists towards artificial intelligence for the diagnosis of retinal disease: A cross-sectional mail-out survey.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2022
    Article
  13. Article
  14. International Teledermatology Review.Current dermatology reports · 2021
    Review
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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

14 authors at 7 institutions in 2 countries.

Changbing ShenDepartment of Dermatology, China-Japan Friendship Hospital, Beijing, China.
Chengxu LiDepartment of Dermatology, China-Japan Friendship Hospital, Beijing, China.
Feng XuDepartment of Dermatology, Huashan Hospital, Fudan University, Shanghai, China.
Ziyi WangDepartment of Dermatology, China-Japan Friendship Hospital, Beijing, China.
Xue ShenDepartment of Dermatology, Chengdu Second People's Hospital, Chengdu, China.
Jing GaoDepartment of Dermatology, the Second Affiliated Hospital, Anhui Medical University, Hefei, China.
Randy KoThe University of New Mexico School of Medicine, Albuquerque, NM, USA.
Yan JingDepartment of Dermatology, the First Affiliated Hospital, Anhui Medical University, Hefei, China.
Xiaofeng TangShanghai Wheat Color Intelligent Technology Company, LTD, Shanghai, China.
Ruixing YuDepartment of Dermatology, China-Japan Friendship Hospital, Beijing, China.
Junhu GuoShanghai Wheat Color Intelligent Technology Company, LTD, Shanghai, China.
Feng XuShanghai Wheat Color Intelligent Technology Company, LTD, Shanghai, China.
Rusong MengDepartment of Dermatology, Specialty Medical Center of the Air Force, Chinese People's Liberation Army, Beijing, China.
Yong CuiDepartment of Dermatology, China-Japan Friendship Hospital, Beijing, China.
China-Japan Friendship Hospital · CNAnhui Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNHuashan Hospital · CNChengdu Second People's Hospital · CNHarvard University · USUniversity of New Mexico · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has become a powerful tool and is attracting more attention in the field of medicine. There are a number of AI studies focusing on skin diseases, and there are many AI products that have been applied in dermatology. However, the attitudes of dermatologists, specifically those from China, towards AI, is not clear as few, if any studies have focused on this issue.

methodsA web-based questionnaire was designed by experts from the Chinese Skin Image Database (CSID) and published on the UMER Doctor platform (an online learning platform for dermatologists developed by the Shanghai Wheat Color Intelligent Technology Company, China). A total of 1,228 Chinese dermatologists were recruited and provided answers to the questionnaire online. The differences of dermatologists' attitudes towards AI among the different groups (stratified by age, gender, hospital level, education degree, professional title, and hospital ownership) were compared by using the Mann-Whitney U test and the Kruskal-Wallis H test. The correlations between stratified factors and dermatologists' attitudes towards AI were calculated by using the Spearman's rank correlation test. SPSS (version 22.0) was utilized for all analyses. A two-sided P value <0.05 was considered statistically significant in all analyses.

resultsA total of 1,228 Chinese dermatologists from 30 provinces, autonomous regions, municipalities, and other regions (including Hong Kong, Macau, and Taiwan) participated in this survey. The dermatologists who participated acquired AI-related information mainly through the Internet, meetings or forums, and 70.51% of participated dermatologists acquired AI-related information by two or more approaches. In total, 99.51% of participated dermatologists pay attention (general, passive-active, and active attention) to information pertaining to AI. Stratified analyses revealed statistically significant differences in their attention levels (unconcerned, general, passive-active, and active attention) to AI-related information by gender, hospital level, education degree, and professional title (P values ≤1.79E-02). In total, 95.36% of the participated dermatologists thought the role of AI to be in "assisting the daily diagnosis and treatment activities for dermatologists". Stratified analyses about the thought of AI roles (unconcerned, useless, assist, and replace) showed that there was no statistically significant difference except for the hospital level (P value =4.09E-03). The correlations between stratified factors with attention levels and the opinions of AI roles showed extremely weak correlations. Furthermore, 64.17% of participated dermatologists thought secondary hospitals in China are in most need of the application AI, and 91.78% of participated dermatologists thought the priority implementation of AI should be in skin tumors.

conclusionsThe majority of Chinese dermatologists are interested in AI information and acquired information about AI through a variety of approaches. Nearly all dermatologists are attentive to information on AI and think the role of AI is in "assisting the daily diagnosis and treatment activities for dermatologists". Future AI implementation should be primarily focused on skin tumors and utilized in in secondary hospitals.

Indexed as

artificial intelligence (AI)attitudesChinese dermatologists

Identifiers

PMID32617318
PMCPMC7327314
OpenAlexW2997928374

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.