Vision Foundation Models (VFMs) have emerged as a promising approach in medical imaging, producing broadly applicable systems that can be efficiently adapted across diverse imaging modalities, anatomical regions, and clinical tasks. However, VFMs require extensive training data, and their progress in medical image anal...
Lovre Antonio Budimir, Ming Gong, Alyssa Foong Quinney et al.· 0 citations
Abstract Background Large language model (LLM) agents capable of generating and executing statistical code from natural language may broaden access to clinical data analysis, yet which pipeline stages they perform reliably and which require expert oversight remain poorly defined. Objective This study aimed to evaluate...
Yi-Lan Wu, D. J. Fu, Yu-Kun Zhou et al.· Journal of Medical Internet...· 0 citations
This work introduces a scalable, resource-efficient, and high-performance information extraction pipeline that leverages large language models (LLMs) to address challenges of free-text clinical records and develops a multi-dimensional assessment for deployment in data extraction tasks.
A. Y. Ong, Quang Nguyen, I. Barai et al.· npj Digital Medicine· 1 citation
This work presents AgentSysBench, a benchmark suite and measurement toolkit with ten representative agentic applications and unified systems-level instrumentation, and identifies six properties that distinguish agentic workloads from conventional LLM serving.
Chaokun Chang, Yu-Kun Zhou, Kai-Hua Fu et al.· 8 citations
While AI agents delivered highly efficient, directionally aligned assessments, they did not fully capture the nuances of human clinical judgment and could not substitute for physician-centered evaluation and promise assistive tools that can triage or pre-screen outputs to reduce human burden.
Peilun Shi, Jian Li, Ziqi Yang et al.· npj Digital Medicine· 0 citations
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