Accepted for/Published in: JMIR Research Protocols
Date Submitted: Apr 21, 2025
Date Accepted: Jul 30, 2025
Factors affecting the receptiveness of Chinese internists and surgeons towards artificial intelligence-driven drug prescription: Protocol for a systematic survey study
ABSTRACT
Background:
Little is known about the acceptability of using autonomous artificial intelligence (AI) in drug prescription. We hypothesize one major hurdle against testing AI prescription in clinical settings is that we do not understand physicians’ attitudes towards such AI systems.
Objective:
This study aims to collect systematic data on physicians’ attitudes towards AI prescription in China.
Methods:
We designed a questionnaire to interrogate a diverse range of factors that may affect physicians’ receptiveness to an AI prescription system, including the physician’s personal attributes and perceived importance of various technological, institutional, and governmental attributes. We will focus on hospitals and physicians with higher patient volumes, and the survey will be conducted in two phases. In Phase 1, survey will be limited to the Tianjin metropolitan area, enlisting >250 physicians from approximately 2 tier-1, 3 tier-2, and 3 tier-3 hospitals to respond to the questionnaire. In the subsequent Phase 2 we will survey metropolitan areas in ≥10 additional province-level administrative divisions, with balanced representation of the Pacific coast and the less-developed inland regions and collecting returned questionnaires from >1250 physicians from >15 tier-1, >15 tier-2, and >15 tier-3 hospitals. Clustering, geo-spatial, and multi-variable analyses will be conducted on the response data to identify distinct physician ‘profile-types’, map the composition and variance of physician profile-types across diverse metropolitan areas, and delineate how a physician’s profile-type might affect his/her receptiveness to AI prescription.
Results:
Survey results and their analyses will be published in a peer-review journal.
Conclusions:
We believe data and analytical insights generated from this study will assist policy makers and AI researchers in making informed decisions on whether AI prescription is a valuable research direction and which use cases of AI prescription are more acceptable to physicians.
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