Perceiving a Mind in a Chatbot: Effect of Mind Perception and Social Cues on Co-presence, Closeness, and Intention to Use

Lee, Sang‐Won; Lee, Naeun; Sah, Young June

doi:10.1080/10447318.2019.1699748

Cited by 108 publications

(54 citation statements)

References 45 publications

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“…In that case, they may lose face-to-face cues and personal interactions with physicians and find themselves in a more passive position for making health-related decisions. This finding is consistent with a study in the chatbot context (within the area of AI systems), which indicated that users have stronger feelings of copresence and closeness when the chatbot uses social cues [ 90 ]. In the context of robot care, a study showed that when robots are used in rehabilitation, they are viewed by patients as reducing human contact [ 91 ].…”

Section: Discussionsupporting

confidence: 91%

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Esmaeilzadeh¹,

Mirzaei²,

Dharanikota³

2021

J Med Internet Res

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Background It is believed that artificial intelligence (AI) will be an integral part of health care 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 have invested in producing AI clinical applications. Patients are one of the most important beneficiaries who potentially interact with these technologies and applications; thus, patients’ perceptions may affect the widespread use of clinical AI. Patients should be ensured that AI clinical applications will not harm them, and that they will instead benefit from using AI technology for health care purposes. Although human-AI interaction can enhance health care outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care. Objective The main objective of this study was to examine how potential users (patients) perceive the benefits, risks, and use of AI clinical applications for their health care purposes and how their perceptions may be different if faced with three health care service encounter scenarios. Methods We designed a 2×3 experiment that crossed a type of health condition (ie, acute or chronic) with three different types of clinical encounters between patients and physicians (ie, AI clinical applications as substituting technology, AI clinical applications as augmenting technology, and no AI as a traditional in-person visit). We used an online survey to collect data from 634 individuals in the United States. Results The interactions between the types of health care service encounters and health conditions significantly influenced 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 found no significant differences among scenarios regarding perceptions of performance risk and social biases. Conclusions The results imply that incompatibility with instrumental, technical, ethical, or regulatory values can be a reason for rejecting AI applications in health care. Thus, there are still various risks associated with implementing AI applications in diagnostics and treatment recommendations for patients with both acute and chronic illnesses. The concerns are also evident if the AI applications are used as a recommendation system under physician experience, wisdom, and control. Prior to the widespread rollout of AI, more studies are needed to identify the challenges that may raise concerns for implementing and using AI applications. This study could provide researchers and managers with critical insights into the determinants of individuals’ intention to use AI clinical applications. Regulatory agencies should establish normative standards and evaluation guidelines for implementing AI in health care in cooperation with health care institutions. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI clinical applications.

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Section: Discussionsupporting

confidence: 91%

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Esmaeilzadeh¹,

Mirzaei²,

Dharanikota³

2021

J Med Internet Res

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“…Therefore, they may lose face-to-face cues and personal interactions with physicians and find themselves in a more passive position for making health-related decisions. This finding is consistent with a study in the chatbot context (within the area of AI systems), which indicates that users have stronger feelings of co-presence and closeness when the chatbot uses social cues [77]. In the context of robot care, a study shows that when robots used in rehabilitation, they are viewed by patients as reducing human contact [78].…”

Section: Discussionsupporting

confidence: 86%

Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives

Esmaeilzadeh

2020

BMC Med Inform Decis Mak

231

144

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Background Several studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning. 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. Thus, many technology companies and governmental projects have invested in producing AI-based clinical tools and medical applications. Patients can be one of the most important beneficiaries and users of AI-based applications whose 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 AI can enhance healthcare outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care. Methods We develop a model mainly based on value perceptions due to the specificity of the healthcare field. This study aims at examining the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers’ perspectives. We use an online survey to collect data from 307 individuals in the United States. Results The proposed model identifies the sources of motivation and pressure for patients in the development of AI-based devices. The results show that technological, ethical (trust factors), and regulatory concerns significantly contribute to the perceived risks of using AI applications in healthcare. Of the three categories, technological concerns (i.e., performance and communication feature) are found to be the most significant predictors of risk beliefs. Conclusions This study sheds more light on factors affecting perceived risks and proposes some recommendations on how to practically reduce these concerns. The findings of this study provide implications for research and practice in the area of AI-based CDS. Regulatory agencies, in cooperation with healthcare institutions, should establish normative standard 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.

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“…For instance, in the context of autonomous vehicles, human characteristics refer to the capacity for rational thought and conscious feeling [21]. In the context of chatbots, Lee et al [45] viewed human characteristics as users' mental states (e.g., intention and consciousness). In the context of social robots, humanlike characteristics refer to human appearance, which includes psychological (e.g., emotions, personalities, and gestures) and nonpsychological features (e.g., head, eyes, arms, and legs) [22].…”

Section: What Is Anthropomorphism?mentioning

confidence: 99%

“…[26] "Humanlike features (e.g., humanlike appearance, emotions, personalities, and behaviors) of a product" [3,14,22] "The extent to which service robots simulate the characteristics, behaviors or appearances of humans" [27] An inference "Individuals' inferences that a chatbot's mental states are similar to those of a human" [45] "Inductive inference in which the perceiver attributes humanlike characteristics, motivations, intentions or underlying mental states to a non-human entity" [11] Other "The assignment of human traits and characteristics to conversation agents"…”

Section: Categorymentioning

confidence: 99%

“…Our analysis also indicates that anthropomorphism has an influence on AIET adoption. Some studies have found a positive relationship between anthropomorphism and adoption intention [e.g., 11,26,45], whereas others have reported a negative relationship [3,14,22]. Furthermore, anthropomorphism has been suggested to affect AIET adoption through various mechanisms [3,12,14,16,42], such as users' attitudes toward [16], trust in [12,42], and enjoyment derived from AIET [12].…”

Section: What Are the Consequences Of Anthropomorphism?mentioning

confidence: 99%

See 1 more Smart Citation

Machinelike or Humanlike? A Literature Review of Anthropomorphism in AI-Enabled Technology

Li¹,

Suh²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Due to the recent proliferation of AI-enabled technology (AIET), the concept of anthropomorphism, human likeness in technology, has increasingly attracted researchers' attention. Researchers have examined how anthropomorphism influences users' perception, adoption, and continued use of AIET. However, researchers have yet to agree on how to conceptualize and operationalize anthropomorphism in AIET, which has resulted in inconsistent findings. A comprehensive understanding is thus needed of the current state of research on anthropomorphism in AIET contexts. To conduct an in-depth analysis of the literature on anthropomorphism, we reviewed 35 empirical studies focusing on conceptualizing and operationalizing AIET anthropomorphism, and its antecedents and consequences. Based on our analysis, we discuss potential research gaps and offer directions for future research.

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Perceiving a Mind in a Chatbot: Effect of Mind Perception and Social Cues on Co-presence, Closeness, and Intention to Use

Cited by 108 publications

References 45 publications

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives

Machinelike or Humanlike? A Literature Review of Anthropomorphism in AI-Enabled Technology

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