A Conceptual Framework for User Trust in AI Biosensors: Integrating Cognition, Context, and Contrast

Abstract

Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition-Context-Contrast (CCC) conceptual framework to explain the trust and acceptance of AI-enabled sensors. First, we map cognition, comprising the expectations and stereotypes that humans have about machines. Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. We then derive practical implications such as enhancing interpretability, tailoring data presentations to task demonstrability, and implementing transitional introduction phases. The framework offers researchers, engineers, and clinicians a structured conceptual framework for designing and implementing the next generation of AI biosensors.

Keywords: artificial intelligence; human–machine interaction; trust.

Figure 1

The CCC Model: Task Context sets a baseline level of trust. User Cognition and Contrast both act on Trust directly and affect each other.

Figure 2

Intellective tasks (right) have clear ground truth (e.g., arrhythmia beats per hour [10]). Judgmental tasks (left) rely on subjective interpretation (e.g., AI pain scores in non-verbal dementia [27,33]). Mid-band examples blend both—sleep-tracker consensus still needs user appraisal [49], while cough-rate or stress-index sensors [9,56] combine objective signals with situational user feelings. Positioning of sensor technologies is for illustration only; each sensor and use-case will exist in a unique intellective–judgmental environment.