Evidence map›Paper›PMID 41403578›Full record

ArticleHealth science reports2025

An Investigation Into the Comprehension, Perceptions, Application, and Determinants Affecting the Utilization of Deepseek Artificial Intelligence by Healthcare Professionals: A Cross-Sectional Study.

Mei Cha, Qin Zhang, Linlin Yu, Hairong Wang, Erdan Luo, Nan Huang, Wenjie Qing, Wenxu Yang, Mengjun Luo, Yiting Du and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

20 authors.

Mei ChaDepartment of Public Health, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.ORCID https://orcid.org/0009-0002-3700-5211
Qin ZhangDepartment of Public Health, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Linlin YuHospital affair department, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Hairong WangSchool of Public Health Southwest Medical University No. 1, Section 1, Xianglin Road Luzhou Sichuan China.
Erdan LuoGCP Institution, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Nan HuangAdministration Office of Taisheng South Road Branch, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Wenjie QingDepartment of Obstetrics and Gynecology, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Wenxu YangDepartment of Child Health Care, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Mengjun LuoClinical Laboratory, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Yiting DuEmergency department, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Yuanhu LuInformation Center, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Haibo YaoMedical department, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Jinghua YeDepartment of Public Health, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Yinghong FanRespiratory department, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Qionghua HuangHospital Infection Control Department The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University) Chengdu China.
Xiaobing RenLeadership Office of the Institute, The People's Hospital Of Jianyang City Chengdu Sichuan China.
Jie LuHospital Infection Control Department Chengdu East New Area Second Peoples Hospital Chengdu Sichuan China.
Liangyin DengPsychiatry Department Tongyi Hospital of Pengzhou City Chengdu Sichuan China.
Jianghui CaiDepartment of medicine, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.
Yonghong LinDepartment of surgery, Chengdu Women's and Children's Central Hospital, School of Medicine University of Electronic Science and Technology of China Chengdu Sichuan China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: DeepSeek LLM, launched on January 5, 2024, integrates natural language processing, data analytics, and medical picture analysis. The use of it among frontline healthcare personnel in Western China has not been previously examined. This study evaluates Chengdu clinicians' knowledge, attitudes, and usage habits on DeepSeek while identifying significant impacting factors. Methods: A WeChat-based survey was administered to physicians, nurses, medical technicians, and administrative personnel at two tertiary maternity and pediatric hospitals in Chengdu from February 24 to 26, 2025. The gathered data encompassed demographics, educational attainment, job designation, familiarity with DeepSeek, frequency of use, openness to training, and preferred functionalities. Of the 865 distributed questionnaires, 788 valid replies (91.10%) were analyzed using SPSS 25. Group disparities were analyzed using chi-square tests, and forward likelihood-ratio logistic regression determined predictors of awareness, utilization, and willingness to undergo training ( Results: A total of 94.2% of participants indicated awareness of DeepSeek, predominantly via media sources (83.65%) and peer endorsements (49.53%). Usage was indicated by 76.14% of respondents, comprising 98.6% of physicians and 89.61% of individuals with a master's degree or higher. The desire to train attained 87.44%, with predominant preferences for hands-on practice (99.4%), a platform overview (93.1%), and case study talks (90.4%). Univariate analyses revealed substantial disparities in awareness, utilization, and training interest based on education level, job title, and job type ( Conclusion: There were substantial deficiencies in the awareness and utilization of DeepSeek among healthcare professionals in Chengdu. Educational attainment, professional status, and occupation significantly influence outcomes. Customized training and assistance are essential to facilitate wider and more successful implementation.

Indexed as

artificial intelligencecomprehension, attitudesDeepSeekhealthcare professionalsusage behaviors

Identifiers

PMID41403578
PMCPMC12703043

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.