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20242026
most citedMedCoT: Medical Chain of Thought via Hierarchical Expert

1 citations · 4 across the 25 of their papers we have counts for

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13 papers · 1 filter

cs.AI2026

CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents

Xiwei Dai, Zijie Meng, Zhiting Fan +4

Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquired, integrated, and evaluated…

cs.CV2026

MedUP: Awakening Unified Understanding and Perception in Medical Vision-Language Models

Yuan Wang, Hualiang Wang, Yixin Chen +6

Medical Vision-Language Models (Med-VLMs) excel at verbalizing visual content, yet precise visual perception, segmentation, and grounding remain challenging. Existing approaches ei…

cs.CV2026

FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening

Bin Pu, Jiewen Yang, Liwen Wang +9

A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources. Currently, fetal ultrasound screening is…

cs.CV2026

ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding

Hangjie Yuan, Yichen Qian, Zhiwei Tang +21

Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric chall…

cs.AI2026

LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making

Zihan Xu, Yanzhen Chen, Xiaocheng Zhang +4

In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making. Prior evaluations of LLM-based medical agents have largely emph…

cs.CV2026

MedStreamBench: A Time-Aware Benchmark for Streaming and Proactive Medical Video Understanding

Yuan Wang, Shujian Gao, Songtao Jiang +2

Existing medical video benchmarks primarily evaluate whether a model produces the correct answer, but rarely assess whether it answers at the right time. In real clinical settings,…

cs.CE2026

AtomiMed: Hierarchical Atomic Fact-Checking for Universal Clinical-Aware Medical Report Evaluation

Yuan Wang, Wanxing Chang, Songtao Jiang +8

Traditional metrics for Medical Report Generation (MRG) predominantly rely on surface-level n-gram overlap, which fails to capture clinical factual accuracy and often overlooks cat…

cs.CV2026

BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming

Jiaxiang Liu, Tianxiang Hu, Juwei Guan +5

Recent advances in vision-language models (VLMs) such as CLIP have demonstrated strong generalization across natural-image domains. However, adapting these models to biomedical ima…

cs.CV2026

Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs

Wen Ma, Fucheng Niu, Zhiting Fan +3

Vision-language models have demonstrated impressive capabilities in general medical visual question answering, yet due to limited interpretability, it remains unclear whether their…

cs.CV2026

HICT: High-precision 3D CBCT reconstruction from a single X-ray

Wen Ma, Jiaxiang Liu, Zikai Xiao +3

Accurate 3D dental imaging is vital for diagnosis and treatment planning, yet CBCT's high radiation dose and cost limit its accessibility. Reconstructing 3D volumes from a single l…

cs.CL2026

Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation

Zhiting Fan, Ruizhe Chen, Tianxiang Hu +7

Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive…

cs.CV2026

Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

Songtao Jiang, Sibo Song, Chenyi Zhou +12

The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over temporal dynamics such as motion…

cs.CV2026

Bridging the Skill Gap in Clinical CBCT Interpretation with CBCTRepD

Qinxin Wu, Fucheng Niu, Hengchuan Zhu +9

Generative AI has advanced rapidly in medical report generation; however, its application to oral and maxillofacial CBCT reporting remains limited, largely because of the scarcity…