JMIR Publications

ABSTRACT

The healthcare management and the medical practitioner literature are missing a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and Artificial Intelligence (AI) systems. Since most of the existing literature have been investigating AIs’ performance and effectiveness from a statistical (analytical) standpoint, there exist lack of studies ensuring AIs’ ecological validity. In this study, we derive a framework that specifically focuses on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model, and expectancy theory. The framework can be used to perform quantitative analyses to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. The proposed framework, if leveraged can help in improving AI acceptance, and use while safeguarding the patients. Overall, this study discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature constituting a socio-cognitive approach extending the theories of distributed cognition and, in so doing, shall account for the ecological validity of AI.