We introduce a novel framework that seamlessly integrates amortized Bayesian inference and active data acquisition, featuring adaptive strategies that can optimize for diverse, user-specified learning objectives at deployment.

This study introduces a framework combining Bayesian Experimental Design with Large Language Models to actively inform medical test selection.