Artificial Intelligence Adoption in Clinical Practice in China: Patterns and Challenges
Author(s)
Amanda Woo, PhD1, Adele Li, MBA2.
1Oracle Life Sciences, Singapore, Singapore, 2Cerner Enviza, Shanghai, China.
1Oracle Life Sciences, Singapore, Singapore, 2Cerner Enviza, Shanghai, China.
OBJECTIVES: Given the rapidly increasing adoption of artificial intelligence in healthcare, this study seeks to understand the current landscape of AI tools integration within contemporary clinical workflows among physicians in China.
METHODS: This cross-sectional study used the 2024 data from the longitudinal Digital Life Physician (DLP) survey (Oracle-JKT) conducted among physicians (N=4,700) from over 20 specialties across China. Outcomes associated with physicians’ online and AI adoption and usage practices for medical-related activities were reported descriptively.
RESULTS: Physicians had spent an average of 36 hours weekly on digital engagement, with 52% of the online time spent on professional-related activities. Among the physicians, 56% reported regular engagement with medical AI, spending at least 2.4 hours per week dedicated to medical applications. Patient management was the most common function for adopting medical-related AI tools (85%), followed by diagnosis and clinical decision-making (84%), self-directed learning (83%), pre-consultation patient interactions (69%), and surgical workflow automation and optimization (69%). Despite relatively widespread adoption of AI tools, user satisfaction is suboptimal. Personalized patient education tools received the highest satisfaction rating (57%), followed by medical information search (55%) and medication delivery robots (54%); the satisfaction towards AI tools for most medical-related functions fall below benchmark expectations (cut-off 80%). Majority of physicians (86%) identified areas for improvement in the capabilities of artificial intelligence within medical fields including medical information search (26%), literature curation (12%) and disease-assisted diagnosis (8%).
CONCLUSIONS: The findings revealed significant integration of AI tools for medical-related activities and workflows, particularly for patient management and diagnosis. However, user satisfaction was suboptimal, with areas for improvement associated with information access and diagnostic support. This indicates a need to focus on enhancing the utility and physician experienced of medical AI in China.
METHODS: This cross-sectional study used the 2024 data from the longitudinal Digital Life Physician (DLP) survey (Oracle-JKT) conducted among physicians (N=4,700) from over 20 specialties across China. Outcomes associated with physicians’ online and AI adoption and usage practices for medical-related activities were reported descriptively.
RESULTS: Physicians had spent an average of 36 hours weekly on digital engagement, with 52% of the online time spent on professional-related activities. Among the physicians, 56% reported regular engagement with medical AI, spending at least 2.4 hours per week dedicated to medical applications. Patient management was the most common function for adopting medical-related AI tools (85%), followed by diagnosis and clinical decision-making (84%), self-directed learning (83%), pre-consultation patient interactions (69%), and surgical workflow automation and optimization (69%). Despite relatively widespread adoption of AI tools, user satisfaction is suboptimal. Personalized patient education tools received the highest satisfaction rating (57%), followed by medical information search (55%) and medication delivery robots (54%); the satisfaction towards AI tools for most medical-related functions fall below benchmark expectations (cut-off 80%). Majority of physicians (86%) identified areas for improvement in the capabilities of artificial intelligence within medical fields including medical information search (26%), literature curation (12%) and disease-assisted diagnosis (8%).
CONCLUSIONS: The findings revealed significant integration of AI tools for medical-related activities and workflows, particularly for patient management and diagnosis. However, user satisfaction was suboptimal, with areas for improvement associated with information access and diagnostic support. This indicates a need to focus on enhancing the utility and physician experienced of medical AI in China.
Conference/Value in Health Info
2025-09, ISPOR Real-World Evidence Summit 2025, Tokyo, Japan
Value in Health Regional, Volume 49S (September 2025)
Code
RWD143
Topic Subcategory
Reproducibility & Replicability
Disease
No Additional Disease & Conditions/Specialized Treatment Areas