Attitudes and perceptions of Chinese oncologists towards artificial intelligence in healthcare: a cross-sectional survey.

Front Digit Health

Department of Breast Oncology, Chongqing University Cancer Hospital and Chongqing University School of Medicine, Institute of Intelligent Oncology, Chongqing University, Chongqing, China.

Published: September 2024

Background: Artificial intelligence (AI) is transforming healthcare, yet little is known about Chinese oncologists' attitudes towards AI. This study investigated oncologists' knowledge, perceptions, and acceptance of AI in China.

Methods: A cross-sectional online survey was conducted among 228 oncologists across China. The survey examined demographics, AI exposure, knowledge and attitudes using 5-point Likert scales, and factors influencing AI adoption. Data were analyzed using descriptive statistics and chi-square tests.

Results: Respondents showed moderate understanding of AI concepts (mean 3.39/5), with higher knowledge among younger oncologists. Only 12.8% used ChatGPT. Most (74.13%) agreed AI is beneficial and could innovate healthcare, 52.19% respondents expressed trust in AI technology. Acceptance was cautiously optimistic (mean 3.57/5). Younger respondents (∼30) show significantly higher trust ( = 0.004) and acceptance ( = 0.009) of AI compared to older respondents, while trust is significantly higher among those with master's or doctorate vs. bachelor's degrees ( = 0.032), and acceptance is higher for those with prior IT experience ( = 0.035).Key drivers for AI adoption were improving efficiency (85.09%), quality (85.53%), reducing errors (84.65%), and enabling new approaches (73.25%).

Conclusions: Chinese oncologists are open to healthcare AI but remain prudently optimistic given limitations. Targeted education, especially for older oncologists, can facilitate AI implementation. AI is largely welcomed for its potential to augment human roles in enhancing efficiency, quality, safety, and innovations in oncology practice.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405309PMC
http://dx.doi.org/10.3389/fdgth.2024.1371302DOI Listing

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