JMIR Publications

ABSTRACT

Background:

Several studies highlighted the effects of artificial intelligence (AI) systems on healthcare delivery. It is believed that AI will be an integral part of healthcare services in the near future and will be incorporated into several aspects of clinical care such as prognosis, diagnostics, and care planning. Thus, many technology companies and governmental projects have invested in producing AI-based clinical tools and medical applications. Patients are one of the most important beneficiaries who potentially interact with AI-based applications, and their perceptions may affect the widespread use of AI-based tools. Patients should be ensured that they will not be harmed by AI-based devices, and instead, they will be benefited by using AI technology for healthcare purposes. Although human-AI interaction can enhance healthcare outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care.

Objective:

This study's main objective is to examine how potential users (individuals) perceive benefits, risks, and use of AI-based devices for their healthcare purposes and how their perceptions may be different if faced with the three healthcare service encounters.

Methods:

We design a 2 by 3 experiment that crosses a type of health condition (i.e., acute or chronic) with three different types of clinical encounters (i.e., only AI-based tools, AI tools in addition to physician’ interaction, or only physicians). We use an online survey to collect data from 634 individuals in the United States.

Results:

Findings demonstrate that the interactions between the types of healthcare service encounters and health conditions significantly influence individuals’ perceptions of privacy concerns, trust issues, communication barriers, concerns about transparency in regulatory standards, liability risks, benefits, and intention to use across the six scenarios. We find no significant difference between scenarios regarding perceptions of performance risk and social biases.

Conclusions:

Our results show that there are still various risks associated with implementing AI-based tools in diagnostics and treatment recommendations for patients with both acute and chronic illnesses. The concerns are also noticeable even if the AI-enabled services are used as a recommendation system under physician experience, wisdom, and control. Prior to the widespread rollout of AI, more studies need to be performed to identify the challenges that may raise concerns for implementing and using AI tools. The findings of this study provide implications for research and practice in the area of AI medical applications. In cooperation with healthcare institutions, regulatory agencies should establish normative standards and evaluation guidelines for the implementation and use of AI in healthcare. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI-based services.