3 citations · 7 across the 14 of their papers we have counts for
15 papers
MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
Rezarta Islamaj, Robert Leaman, Joey Chan +13
Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabil…
Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization
Chih-Hsuan Wei, Chi-Ping Day, Zhizheng Wang +8
Drug repurposing is often framed as a candidate identification task, but existing approaches provide limited guidance for distinguishing biologically plausible candidates from hist…
DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
Zhizheng Wang, Chih-Hsuan Wei, Joey Chan +19
Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent deep research systems aim to…
Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution
Qiao Jin, Yin Fang, Lauren He +12
Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automat…
CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography
Qingqing Zhu, Qiao Jin, Tejas S. Mathai +10
Artificial intelligence (AI) can automatically delineate lesions on computed tomography (CT) and generate radiology report content, yet progress is limited by the scarcity of publi…
Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
Jash Rajesh Parekh, Wonbin Kweon, Joey Chan +7
Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every…
Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis
Zhizheng Wang, Yifan Yang, Qiao Jin +1
The gene set analysis (GSA) is a foundational approach for uncovering the molecular functions associated with a group of genes. Recently, LLM-powered methods have emerged to annota…
Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma +3
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailin…
Knowledge-guided Contextual Gene Set Analysis Using Large Language Models
Zhizheng Wang, Chi-Ping Day, Chih-Hsuan Wei +9
Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook cl…
Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare
Yifan Yang, Qiao Jin, Qingqing Zhu +5
Large Language Models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in…
Entry-level guide to the use of large language models for medical research
Qiao Jin, Nicholas Wan, Robert Leaman +20
Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing variou…
GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases
Zhizheng Wang, Qiao Jin, Chih-Hsuan Wei +6
Gene set knowledge discovery is essential for advancing human functional genomics. Recent studies have shown promising performance by harnessing the power of Large Language Models…
How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses
Qingqing Zhu, Benjamin Hou, Tejas S. Mathai +7
Automatically interpreting CT scans can ease the workload of radiologists. However, this is challenging mainly due to the scarcity of adequate datasets and reference standards for…
AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning
Qiao Jin, Zhizheng Wang, Yifan Yang +8
Clinical calculators play a vital role in healthcare by offering accurate evidence-based predictions for various purposes such as prognosis. Nevertheless, their widespread utilizat…
PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge
Chih-Hsuan Wei, Alexis Allot, Po-Ting Lai +7
PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for…