Trust Recipes for Enhancing the Intention to Adopt Conversational Agents for Disease Diagnosis: An fsQCA Approach

In this study, we examine the configurations of trust-enhancing factors that determine the intention to adopt conversational agents (CAs) for disease diagnosis. After identifying trust factors influencing the behavioral intent to adopt CAs based on the information systems acceptance research field, we assigned 201 participants to use the mobile Ada application and surveyed them about their experience. Ada is a medical diagnostic CA that combines patients’ symptoms with their medical history and provides diagnostic suggestions. The collected data was analyzed using a fuzzy set qualitative comparative analysis to capture the causal complexity of trust. We identified several configurations of trust-enhancing factors affecting the intention to adopt the CA. In particular, our results show that the adoption intentions are strongly determined by trust factors associated with the performance dimension. Furthermore, we derived two propositions for the development of CAs for healthcare purposes and elaborated implications for research and practice

Dissociation Between Users’ Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study

The latest developments in the field of artificial intelligence (AI) have given rise to many ethical and socio-economic concerns. Nonetheless, the impact of AI technologies is evident and tangible in our everyday life. This dichotomy leads to mixed feelings toward AI: people recognize the positive impact of AI, but they also show concerns, especially about their privacy and security. In this article, we try to understand whether the implicit and explicit attitudes toward AI are coherent. We investigated explicit and implicit attitudes toward AI by combining a self-report measure and an implicit measure, i.e., the implicit association test. We analyzed the explicit and implicit responses of 829 participants. Results revealed that while most of the participants explicitly express a positive attitude toward AI, their implicit responses seem to point in the opposite direction. Results also show that, in both the explicit and implicit measures, females show a more negative attitude than males, and people who work in the field of AI are inclined to be positive toward AI

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

Background: Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research. Methods: The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria. Research: 3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face. Conclusion: Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents

A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems

Health care is shifting toward become proactive according to the concept of P5 medicine – a predictive, personalized, preventive, participatory and precision discipline. This patient-centered care heavily leverages the latest technologies of artificial intelligence (AI) and robotics that support diagnosis, decision making and treatment. In this paper, we present the role of AI and robotic systems in this evolution, including example use cases. We categorize systems along multiple dimensions such as the type of system, the degree of autonomy, the care setting where the systems are applied, and the application area. These technologies have already achieved notable results in the prediction of sepsis or cardiovascular risk, the monitoring of vital parameters in intensive care units, or in the form of home care robots. Still, while much research is conducted around AI and robotics in health care, adoption in real world care settings is still limited. To remove adoption barriers, we need to address issues such as safety, security, privacy and ethical principles; detect and eliminate bias that could result in harmful or unfair clinical decisions; and build trust in and societal acceptance of AI

Independence for Whom? A Critical Discourse Analysis of Onboarding a Home Health Monitoring System for Older Adult Care

Home health monitoring systems (HHMS) are presented as a cost-effective solution that will assist with collaborative care of older adults. However, instead of care recipients feeling like collaborators, such systems often disempower them. In this paper, we examine the dissemination, onboarding, and initial use of an HHMS to see how the discourse used by developers and participants affects users' collaborative care efforts. We found that the textual information provided often contrasted with how our participants managed their care. Instead of providing participants with 'independence,' 'safety,' and 'peace of mind,' care recipients were placed in a more dependent, less proactive role, and care providers were pressured to take on more responsibilities. We position HHMS, as they are currently marketed and onboarded, as normalizing pseudo-institutionalization. As an alternative we advocate that the discourse and design of such systems should reflect and re-enforce the varied roles care recipients take in managing their care

Artificial intelligence in healthcare: What to consider

Computers have become an everyday part of our lives. One field on the forefront of computer science research is artificial intelligence. Although some people may have fears of artificial intelligence, the technology can be beneficial. It has the ability to enhance or replace human capabilities in many areas. Artificial intelligence, by definition, is any machine processing simulation of human intelligence. As research begins to produce more accessible and powerful technology that utilizes artificial intelligence, careful deliberations must be given to its implementations. Specifically in the field of healthcare, artificial intelligence has the ability to revolutionize the field. But what considerations must be given before turning over our healthcare decisions to artificial intelligence? The purpose of this research is to evaluate the current studies on artificial intelligence in healthcare and explore the ethical dilemmas surrounding its applications

Artificial Intelligence in Healthcare: Doctor as a Stakeholder

Artificial Intelligence (AI) is making significant inroads into healthcare, as in many other walks of life. Its contribution to clinical decision making, to achieve better outcomes, image interpretation especially in radiology, pathology and oncology, data mining, generating hidden insights, and reducing human errors in healthcare delivery is noteworthy. Yet there are physicians as well as patients and their families, who are wary of its role and its implementation in routine clinical practice. Any discussion on AI and its role in healthcare brings into consideration issues like hype and hope associated with any new technologies, uncertain understanding of who the stakeholders are, patients’ views and their acceptance, validity of data models used for training and decision making at the point of care. These considerations must be accompanied by thorough policy discussions on the future of AI in healthcare and how the curriculum planners in medical education should train the medical students who are the future healthcare providers. A deliberation on the issues on the issues that are common to Information Technology (IT) like cybersecurity, ethics and legal aspects, privacy, and transparency is also needed