Zhang Zhan, Citardi Daniel, Wang Dakuo, Genc Yegin, Shan Juan, Fan Xiangmin
Pace University, USA.
IBM Research, USA.
Health Informatics J. 2021 Apr-Jun;27(2):14604582211011215. doi: 10.1177/14604582211011215.
Results of radiology imaging studies are not typically comprehensible to patients. With the advances in artificial intelligence (AI) technology in recent years, it is expected that AI technology can aid patients' understanding of radiology imaging data. The aim of this study is to understand patients' perceptions and acceptance of using AI technology to interpret their radiology reports. We conducted semi-structured interviews with 13 participants to elicit reflections pertaining to the use of AI technology in radiology report interpretation. A thematic analysis approach was employed to analyze the interview data. Participants have a generally positive attitude toward using AI-based systems to comprehend their radiology reports. AI is perceived to be particularly useful in seeking actionable information, confirming the doctor's opinions, and preparing for the consultation. However, we also found various concerns related to the use of AI in this context, such as cyber-security, accuracy, and lack of empathy. Our results highlight the necessity of providing AI explanations to promote people's trust and acceptance of AI. Designers of patient-centered AI systems should employ user-centered design approaches to address patients' concerns. Such systems should also be designed to promote trust and deliver concerning health results in an empathetic manner to optimize the user experience.
放射影像学研究结果通常患者难以理解。近年来随着人工智能（AI）技术的进步，预计AI技术能够帮助患者理解放射影像学数据。本研究的目的是了解患者对使用AI技术解读其放射学报告的看法和接受程度。我们对13名参与者进行了半结构化访谈，以引出与在放射学报告解读中使用AI技术相关的看法。采用主题分析法对访谈数据进行分析。参与者对使用基于AI的系统来理解其放射学报告总体持积极态度。人们认为AI在获取可采取行动的信息、确认医生的意见以及为会诊做准备方面特别有用。然而，我们也发现了在这种情况下与使用AI相关的各种担忧，如网络安全、准确性和缺乏同理心。我们的结果强调了提供AI解释以促进人们对AI的信任和接受的必要性。以患者为中心的AI系统设计者应采用以用户为中心的设计方法来解决患者的担忧。此类系统还应设计成能促进信任并以同理心的方式传达有关健康的结果，以优化用户体验。
