Comprehensive evaluation demonstrates that our method significantly outperforms baseline approaches in both assessment accuracy and treatment plan quality.
Xiongxiao Xu, Haoran Wang, Yueqing Liang, Philip S. Yu, Yue Zhao, Kai Shu · Feb 25, 2025 · Citations: 0
Automatic MetricsMedicine
One natural way for humans to detect time series anomalies is through visualization and textual description.
To address the gap, we build a VisualTimeAnomaly benchmark to comprehensively investigate zero-shot capabilities of MLLMs for TSAD, progressively from point-, range-, to variate-wise anomalies, and extends to irregular sampling conditions.
Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evaluating LMs on multiple-choice board exam questions.
This design enables systematic evaluations of model performance and bias by studying how demographic factors affect decision-making.
Zijie Liu, Xinyu Zhao, Jie Peng, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu · Jan 29, 2025 · Citations: 0
Automatic MetricsSimulation EnvMedicine
These tuning methods and benchmarks overlook critical aspects like evidence-based reasoning and handling distracting information.
To bridge this gap, we introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards.
