ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis

Key points

• Clinical diagnosis is inherently sequential: clinicians escalate from cheap to costly tests only when additional evidence is expected to resolve diagnostic uncertainty.1
• Given a frozen, API-accessed vision-language model, ActiveMedAgent tracks probability distributions over candidate diagnoses and scores each acquisition by its per-step diagnostic utility minus cost.1
• A lightweight MLP controller is then trained offline on these scored trajectories, learning when to request additional evidence and when to commit.1
• Across three commonly used benchmarks, trajectory-based policy learning consistently outperforms both unguided acquisition and full-modality baselines.1

Sources (1)

• [1]ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:06 AM

  We present ActiveMedAgent, a framework that brings this cost-aware sequential logic to multimodal medical AI.

  Clinical diagnosis is inherently sequential: clinicians escalate from cheap to costly tests only when additional evidence is expected to resolve diagnostic uncertainty.

Extractive summary: sentences quoted from the sources.