2 citations · 2 across the 3 of their papers we have counts for
6 papers
BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents
Jinge Wu, Hongjian Zhou, Mingde Zeng +8
Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness an…
Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
Hongjian Zhou, Xinyu Zou, Jinge Wu +19
Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increa…
RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction
Fenglin Liu, Jinge Wu, Hongjian Zhou +9
Large Language Models (LLMs) achieve competitive results compared to human experts in medical examinations. However, it remains a challenge to apply LLMs to complex clinical decisi…
Clinical-R1: Empowering Large Language Models for Faithful and Comprehensive Reasoning with Clinical Objective Relative Policy Optimization
Boyang Gu, Hongjian Zhou, Bradley Max Segal +6
Recent advances in large language models (LLMs) have shown strong reasoning capabilities through large-scale pretraining and post-training reinforcement learning, demonstrated by D…
Large Language Models in the Clinic: A Comprehensive Benchmark
Fenglin Liu, Zheng Li, Hongjian Zhou +16
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with…
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Hongjian Zhou, Fenglin Liu, Boyang Gu +16
Large language models (LLMs), such as ChatGPT, have received substantial attention due to their capabilities for understanding and generating human language. While there has been a…