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Satellite imagery is proposed as a novel pretraining domain for MedVFM development and benchmarking, motivated by its closer visual alignment with medical data and its freedom from the privacy constraints that limit medical datasets.
LLM agents perform reliably for question generation and SAP drafting but require expert verification of formula composition, cohort boundary logic, and concordance computation before results are reported.
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.
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.
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.
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