Integrating AI Predictions With Clinician Expertise

Study Design: This is a 2x2 factorial within-subjects design. The two factors are (1) Bayesian updating via continuous likelihood ratios (CLR) vs. standard predicted probability, and (2) uncertainty quantification (95% confidence intervals) vs. point estimate only. AI prediction accuracy (accurate vs. intentionally misleading) is varied as a within-subjects stratification factor balanced across all 4 conditions, with half of each participant's vignettes receiving accurate predictions and half receiving misleading predictions. AI predictions are simulated (pre-programmed) for experimental control. Vignette order and condition assignment are independently randomized per participant. Primary Analysis: Diagnostic accuracy is analyzed using a generalized linear mixed model (GLMM) with fixed effects for CLR, Uncertainty, Misleading, and vignette, and a participant random intercept. Pre-specified secondary analyses examine interactions of presentation format with misleading AI. Sample Size: Simulation-based power analysis (1,000 Monte Carlo iterations per scenario) was conducted using the planned GLMM. Assuming 70% baseline diagnostic accuracy and within-participant ICC of 0.25, the study achieves 85.8% power for the CLR main effect and 85.7% for the Uncertainty main effect with N=100 at alpha=0.05 (two-tailed).