AI in medical diagnosis: A contextualised study of patient motivations and concerns

Abstract

Patients' reactions to the implementation of Artificial Intelligence (AI) in healthcare range from adverse to favourable. While AI holds the promise of revolutionising healthcare by enhancing, accelerating, and improving the precision of care services, our understanding of patients' reactions to these paradigm shifts remains limited. In particular, little is known about the extent to which patients are receptive to independently use AI-enabled applications for diagnosis. This research seeks to develop a holistic, context-specific model capturing both the negative and positive cognitive responses of patients utilising AI-powered diagnostic services. Employing a sequential mixed-methods approach, the study draws on Behavioural Reasoning Theory to decode patients' cognitive reactions, including their reasons for and reasons giants using such applications. The research begins with a qualitative exploration, analysing user reviews to identify context-specific barriers and motivators. Building on these qualitative insights, the model's empirical validity is tested through a quantitative phase involving survey data analysis. Our findings provide a nuanced understanding of the context-dependent factors shaping patients' cognitive responses to AI-enabled diagnostic services, offering valuable insights for the design and implementation of patient-centred AI solutions in healthcare.

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