Trust Calibration in Agentic AI Clinical Decision Support: How Autonomy Level and Transparency Mechanism Shape Clinician Reliance

Agentic artificial intelligence (AI) systems are rapidly entering healthcare. Unlike static AI, agentic systems exert persistent, goal-directed influence over care trajectories, creating new and largely unstudied challenges for clinician trust and reliance calibration. Drawing on trust calibration theory, human-AI complementarity research, and augmented intelligence literature, this paper develops a model examining how two design dimensions of agentic clinical AI, autonomy level and transparency mechanism, shape clinician trust calibration and downstream outcomes, including decision quality, cognitive load, and error detection. We propose an experiment with clinicians using clinical vignettes across three temporally sequenced decision steps. We contribute: a reconceptualization of trust calibration as a dynamic, temporally extended process; a theorization of autonomy level and transparency as orthogonal design dimensions with interacting effects on calibration; and a falsifiable hypothesis set and an experimental design for validation. The proposed study has the potential to advance theory on human-agentic AI collaboration and inform governance design for high-stakes IS deployments.