医务人员和住院医师对使用深度学习进行眼病筛查的偏好：离散选择实验。
Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment.
机构信息
Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.
Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.
出版信息
J Med Internet Res. 2022 Sep 20;24(9):e40249. doi: 10.2196/40249.
BACKGROUND
Deep learning-assisted eye disease diagnosis technology is increasingly applied in eye disease screening. However, no research has suggested the prerequisites for health care service providers and residents willing to use it.
OBJECTIVE
The aim of this paper is to reveal the preferences of health care service providers and residents for using artificial intelligence (AI) in community-based eye disease screening, particularly their preference for accuracy.
METHODS
Discrete choice experiments for health care providers and residents were conducted in Shanghai, China. In total, 34 medical institutions with adequate AI-assisted screening experience participated. A total of 39 medical staff and 318 residents were asked to answer the questionnaire and make a trade-off among alternative screening strategies with different attributes, including missed diagnosis rate, overdiagnosis rate, screening result feedback efficiency, level of ophthalmologist involvement, organizational form, cost, and screening result feedback form. Conditional logit models with the stepwise selection method were used to estimate the preferences.
RESULTS
Medical staff preferred high accuracy: The specificity of deep learning models should be more than 90% (odds ratio [OR]=0.61 for 10% overdiagnosis; P<.001), which was much higher than the Food and Drug Administration standards. However, accuracy was not the residents' preference. Rather, they preferred to have the doctors involved in the screening process. In addition, when compared with a fully manual diagnosis, AI technology was more favored by the medical staff (OR=2.08 for semiautomated AI model and OR=2.39 for fully automated AI model; P<.001), while the residents were in disfavor of the AI technology without doctors' supervision (OR=0.24; P<.001).
CONCLUSIONS
Deep learning model under doctors' supervision is strongly recommended, and the specificity of the model should be more than 90%. In addition, digital transformation should help medical staff move away from heavy and repetitive work and spend more time on communicating with residents.
背景
深度学习辅助眼病诊断技术在眼病筛查中得到了越来越多的应用。然而，目前尚无研究表明医疗服务提供者和居民对使用它的意愿。
目的
本研究旨在揭示医疗服务提供者和居民对社区眼病筛查中使用人工智能（AI）的偏好，特别是对准确性的偏好。
方法
在中国上海，对医疗服务提供者和居民进行了离散选择实验。共有 34 家医疗机构具有足够的 AI 辅助筛查经验参与了研究。共有 39 名医务人员和 318 名居民回答了问卷，并对不同属性的替代筛查策略进行了权衡，包括漏诊率、误诊率、筛查结果反馈效率、眼科医生参与程度、组织形式、成本和筛查结果反馈形式。采用逐步选择法的条件逻辑回归模型进行偏好估计。
结果
医务人员更倾向于高准确性：深度学习模型的特异性应超过 90%（误诊率增加 10%时的优势比[OR]为 0.61；P<.001），远高于食品和药物管理局的标准。然而，准确性并不是居民的偏好。相反，他们更喜欢让医生参与筛查过程。此外，与完全手动诊断相比，医务人员更倾向于使用 AI 技术（半自动 AI 模型的 OR=2.08，全自动 AI 模型的 OR=2.39；P<.001），而居民则不赞成没有医生监督的 AI 技术（OR=0.24；P<.001）。
结论
建议在医生监督下使用深度学习模型，且模型的特异性应超过 90%。此外，数字化转型应帮助医务人员摆脱繁重和重复的工作，将更多时间用于与居民沟通。
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