Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark.
Visual Analysis and Perception Lab, Aalborg University, Aalborg, Denmark.
J Med Internet Res. 2021 Dec 13;23(12):e26611. doi: 10.2196/26611.
Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public's interests in such features of AI.
This study elicited the public's preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI.
We conducted a choice-based conjoint survey of public preferences for attributes of AI decision making in health care in a representative sample of the adult Danish population. Initial focus group interviews yielded 6 attributes playing a role in the respondents' views on the use of AI decision support in health care: (1) type of AI decision, (2) level of explanation, (3) performance/accuracy, (4) responsibility for the final decision, (5) possibility of discrimination, and (6) severity of the disease to which the AI is applied. In total, 100 unique choice sets were developed using fractional factorial design. In a 12-task survey, respondents were asked about their preference for AI system use in hospitals in relation to 3 different scenarios.
Of the 1678 potential respondents, 1027 (61.2%) participated. The respondents consider the physician having the final responsibility for treatment decisions the most important attribute, with 46.8% of the total weight of attributes, followed by explainability of the decision (27.3%) and whether the system has been tested for discrimination (14.8%). Other factors, such as gender, age, level of education, whether respondents live rurally or in towns, respondents' trust in health and technology, and respondents' fears and hopes regarding AI, do not play a significant role in the majority of cases.
The 3 factors that are most important to the public are, in descending order of importance, (1) that physicians are ultimately responsible for diagnostics and treatment planning, (2) that the AI decision support is explainable, and (3) that the AI system has been tested for discrimination. Public policy on AI system use in health care should give priority to such AI system use and ensure that patients are provided with information.
某些类型的人工智能（AI），即深度学习模型，在某些领域的表现可以优于医疗保健专业人员。此类模型在提高诊断、治疗和预防效果以及降低医疗成本方面具有巨大的潜力。然而，它们在某种意义上是不透明的，即其确切的推理过程无法完全解释。不同的利益相关者强调了 AI 决策透明度/可解释性的重要性。透明度/可解释性可能会以牺牲性能为代价。因此，需要制定一项公共政策来规范 AI 在医疗保健中的使用，在兼顾社会对高性能以及透明度/可解释性的利益的同时，实现两者之间的平衡。公共政策应该考虑到更广泛的公众对 AI 这些特征的兴趣。
本研究旨在了解公众对 AI 决策在医疗保健中的性能和可解释性的偏好，并确定这些偏好是否取决于受访者的特征，包括对健康和技术的信任以及对 AI 的恐惧和希望。
我们在丹麦成年人的代表性样本中进行了基于选择的联合调查，以了解公众对 AI 决策在医疗保健中的属性的偏好。最初的焦点小组访谈得出了 6 个在受访者对 AI 决策支持在医疗保健中的使用的看法中发挥作用的属性：（1）AI 决策的类型，（2）解释水平，（3）性能/准确性，（4）最终决策的责任，（5）歧视的可能性，以及（6）AI 应用的疾病严重程度。总共使用分数阶因子设计开发了 100 个独特的选择集。在 12 项任务的调查中，受访者被要求根据 3 种不同的情况，就 AI 系统在医院中的使用偏好进行回答。
在 1678 名潜在受访者中，有 1027 名（61.2%）参与了调查。受访者认为医生对治疗决策负有最终责任是最重要的属性，占属性总权重的 46.8%，其次是决策的可解释性（27.3%）和系统是否经过歧视测试（14.8%）。其他因素，如性别、年龄、教育程度、受访者是否居住在农村或城镇、受访者对健康和技术的信任程度以及受访者对 AI 的恐惧和希望程度，在大多数情况下并不起重要作用。
对公众来说最重要的 3 个因素按重要性降序排列依次是：（1）医生对诊断和治疗计划最终负责，（2）AI 决策支持可解释，以及（3）AI 系统经过了歧视测试。AI 系统在医疗保健中的使用的公共政策应该优先考虑此类 AI 系统的使用，并确保向患者提供信息。
