Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence–Driven Drug Prescription: Protocol for a Systematic Survey Study

Authors

Xiaowen Gong (ORCID: https://orcid.org/0000-0001-8332-0402)
Jinyu Wang
Yueshen Ma
Sichang Liu
Junren Chen (ORCID: https://orcid.org/0000-0003-3691-4931)
Qiujin Shen (ORCID: https://orcid.org/0000-0003-2103-4253)
Robert Peter Gale (ORCID: https://orcid.org/0000-0002-9156-1676)
Zetao Ren
Yu Hu
Zhen Song (ORCID: https://orcid.org/0000-0002-3948-1695)
Wei Zhang

Institutions

Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
Institute of Hematology & Blood Diseases Hospital (CN)
Imperial College London (GB)

Publication Details

Journal: JMIR Research Protocols
Published: 2025-08-14
DOI: https://doi.org/10.2196/76009
Citations: 1
Primary Topic: Artificial Intelligence in Healthcare and Education
Type: article
Field-Weighted Citation Impact: 0.65

Funders

National Institute for Health and Care Research
National Natural Science Foundation of China
Chinese Academy of Medical Sciences
Tianjin Science and Technology Committee

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article

Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence–Driven Drug Prescription: Protocol for a Systematic Survey Study

Xiaowen Gong, Jinyu Wang, Yueshen Ma, Sichang Liu et al.

Aug 14, 2025

1 citations

JMIR Research Protocols

Artificial Intelligence in Healthcare and Education

0.65

article

Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence–Driven Drug Prescription: Protocol for a Systematic Survey Study

Xiaowen Gong, Jinyu Wang, Yueshen Ma, Sichang Liu, Junren Chen, Qiujin Shen, Robert Peter Gale, Zetao Ren, Yu Hu, Zhen Song, Wei Zhang

August 14, 2025article en

1 citations

Abstract

No abstract available for this paper.

JMIR Research ProtocolsVol. 14

Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Institute of Hematology & Blood Diseases Hospital (CN), Imperial College London (GB)

National Institute for Health and Care Research, National Natural Science Foundation of China, Chinese Academy of Medical Sciences, Tianjin Science and Technology Committee

Openalex Percentile: Top 23%

Artificial Intelligence in Healthcare and Education

0.65

DOI Open Access PDF OpenAlex

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