Zhang Xiaobo, Gu Ying, Yin Jie, Zhang Yuejie, Jin Cheng, Wang Weibing, Li Albert Martin, Wang Yingwen, Su Ling, Xu Hong, Ge Xiaoling, Ye Chengjie, Tang Liangfeng, Shen Bing, Fang Jinwu, Wang Daoyang, Feng Rui
Children's Hospital of Fudan University, Shanghai, China.
School of Philosophy Fudan University, Shanghai, China.
JMIR Form Res. 2023 Oct 26;7:e42202. doi: 10.2196/42202.
Medical artificial intelligence (AI) has significantly contributed to decision support for disease screening, diagnosis, and management. With the growing number of medical AI developments and applications, incorporating ethics is considered essential to avoiding harm and ensuring broad benefits in the lifecycle of medical AI. One of the premises for effectively implementing ethics in Medical AI research necessitates researchers' comprehensive knowledge, enthusiastic attitude, and practical experience. However, there is currently a lack of an available instrument to measure these aspects.
The aim of this study was to develop a comprehensive scale for measuring the knowledge, attitude, and practice of ethics implementation among medical AI researchers, and to evaluate its measurement properties.
The construct of the Knowledge-Attitude-Practice in Ethics Implementation (KAP-EI) scale was based on the Knowledge-Attitude-Practice (KAP) model, and the evaluation of its measurement properties was in compliance with the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) reporting guidelines for studies on measurement instruments. The study was conducted in 2 phases. The first phase involved scale development through a systematic literature review, qualitative interviews, and item analysis based on a cross-sectional survey. The second phase involved evaluation of structural validity and reliability through another cross-sectional study.
The KAP-EI scale had 3 dimensions including knowledge (10 items), attitude (6 items), and practice (7 items). The Cronbach α for the whole scale reached .934. Confirmatory factor analysis showed that the goodness-of-fit indices of the scale were satisfactory (χ/df ratio:=2.338, comparative fit index=0.949, Tucker Lewis index=0.941, root-mean-square error of approximation=0.064, and standardized root-mean-square residual=0.052).
The results show that the scale has good reliability and structural validity; hence, it could be considered an effective instrument. This is the first instrument developed for this purpose.
医学人工智能（AI）在疾病筛查、诊断和管理的决策支持方面做出了重大贡献。随着医学人工智能开发和应用数量的不断增加，纳入伦理考量被认为对于在医学人工智能的生命周期中避免危害并确保广泛受益至关重要。在医学人工智能研究中有效实施伦理的前提之一是研究人员需要具备全面的知识、积极的态度和实践经验。然而，目前缺乏一种可用的工具来衡量这些方面。
本研究的目的是开发一个综合量表，用于测量医学人工智能研究人员在伦理实施方面的知识、态度和实践情况，并评估其测量属性。
伦理实施中的知识-态度-实践（KAP-EI）量表的构建基于知识-态度-实践（KAP）模型，其测量属性的评估符合基于共识的健康状况测量工具选择标准（COSMIN）关于测量工具研究的报告指南。该研究分两个阶段进行。第一阶段包括通过系统文献综述、定性访谈以及基于横断面调查的项目分析来开发量表。第二阶段包括通过另一项横断面研究评估结构效度和信度。
KAP-EI量表有3个维度，包括知识（10项）、态度（6项）和实践（7项）。整个量表的Cronbach α系数达到0.934。验证性因素分析表明该量表的拟合优度指标令人满意（χ/df比值=2.338，比较拟合指数=0.949，塔克·刘易斯指数=0.941，近似均方根误差=0.064，标准化均方根残差=0.052）。
结果表明该量表具有良好的信度和结构效度；因此，可以认为它是一种有效的工具。这是为此目的开发的首个工具。
