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ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Human Factors and Digital Health
Volume 6 - 2024 |
doi: 10.3389/fdgth.2024.1371302
Attitudes and Perceptions of Chinese Oncologists Towards Artificial Intelligence in Healthcare: A Cross-Sectional Survey
Provisionally accepted- 1 Johns Hopkins University, Baltimore, United States
- 2 Chongqing University, Chongqing, China
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 (p=0.004) and acceptance (p=0.009) of AI compared to older respondents, while trust is significantly higher among those with master's or doctorate versus bachelor's degrees (p=0.032), and acceptance is higher for those with prior IT experience (p=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.
Keywords: artificial intelligence, AI, Oncologists, Attitude, Perception
Received: 16 Jan 2024; Accepted: 13 Aug 2024.
Copyright: © 2024 Li and Xiong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ming Li, Johns Hopkins University, Baltimore, United States
Xiaomin Xiong, Chongqing University, Chongqing, 400030, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.