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ActiveMedAgent framework learns cost-aware medical diagnosis

Researchers have developed ActiveMedAgent, a new framework designed to improve multimodal medical AI by mimicking the cost-aware sequential decision-making process of human clinicians. This agent learns to strategically acquire additional diagnostic evidence, balancing diagnostic utility against acquisition costs. Experiments across three benchmarks demonstrated that ActiveMedAgent consistently outperformed unguided acquisition and full-modality baselines, even identifying an information overload effect where omitting certain data led to correct diagnoses. AI

IMPACT This framework could lead to more efficient and accurate AI-assisted medical diagnoses by optimizing the use of diagnostic resources.

RANK_REASON The cluster contains a research paper detailing a new AI framework for medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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ActiveMedAgent framework learns cost-aware medical diagnosis

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The cluster contains a research paper detailing a new AI framework for medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Weiwei Ma, Xiaobing Yu, Peijie Qiu, Jin Yang, Zhaoqi An, Xuanzhao Dong, Xiaoqi Zhao, Xiaofeng Liu ·

    ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis

    arXiv:2610.11140v1 Announce Type: cross Abstract: Clinical diagnosis is inherently sequential: clinicians escalate from cheap to costly tests only when additional evidence is expected to resolve diagnostic uncertainty. We present ActiveMedAgent, a framework that brings this cost-…