124 citations · 211 across the 17 of their papers we have counts for
18 papers
What Do Biomedical NER and Entity Linking Benchmarks Measure? A Corpus-Centric Diagnostic Framework
Robert Leaman, Rezarta Islamaj, Zhiyong Lu
Biomedical named entity recognition (NER) and entity linking (EL) strongly depend on annotated corpora, but the utility of these resources for benchmarking is often assumed rather…
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…
Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answering
Rezarta Islamaj, Joey Chan, Robert Leaman +13
Multi-hop question answering (QA) remains a significant challenge in the biomedical domain, requiring systems to integrate information across multiple sources to answer complex que…
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…
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…
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…
Enhancing Biomedical Relation Extraction with Directionality
Po-Ting Lai, Chih-Hsuan Wei, Shubo Tian +2
Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemica…
Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
Yifan Yang, Qiao Jin, Robert Leaman +15
The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with…
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…
EnzChemRED, a rich enzyme chemistry relation extraction dataset
Po-Ting Lai, Elisabeth Coudert, Lucila Aimo +16
Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveri…
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…
PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence
Qiao Jin, Robert Leaman, Zhiyong Lu
Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on pr…
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
Ling Luo, Chih-Hsuan Wei, Po-Ting Lai +3
Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mi…
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
Robert Leaman, Rezarta Islamaj, Alexis Allot +3
A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of S…
LitCovid in 2022: an information resource for the COVID-19 literature
Qingyu Chen, Alexis Allot, Robert Leaman +5
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on…
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Qingyu Chen, Alexis Allot, Robert Leaman +35
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design…