ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis

ActiveMedAgent is a cost‑aware framework that guides multimodal medical AI to acquire diagnostic evidence sequentially, mirroring clinical practice of escalating from cheap to costly tests. It uses a frozen vision‑language model to track diagnosis probabilities, scores each potential acquisition by diagnostic utility minus cost, and trains a lightweight MLP controller offline to decide when to request more evidence or to stop. Experiments on three benchmarks show that trajectory‑based policy learning outperforms unguided acquisition and full‑modality baselines, revealing an information overload effect where the agent can correctly diagnose with fewer channels than the full‑modality approach.