5 citations · 11 across the 15 of their papers we have counts for
16 papers
A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation
Ruida Cheng, Tejas S. Mathai, Benjamin Hou +4
In previous work, we integrated large language models (LLMs) into the lesion segmentation model based on the ULS23 DeepLesion dataset, using short-form findings from the reports. I…
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…
ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
Joey Chan, Yikun Han, Jingyuan Chen +8
Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores differences in readers' info…
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…
Text Embedded Swin-UMamba for DeepLesion Segmentation
Ruida Cheng, Tejas Sudharshan Mathai, Pritam Mukherjee +5
Segmentation of lesions on CT enables automatic measurement for clinical assessment of chronic diseases (e.g., lymphoma). Integrating large language models (LLMs) into the lesion s…
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…
Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization
Xiuying Chen, Shen Gao, Mingzhe Li +3
Nowadays, neural text generation has made tremendous progress in abstractive summarization tasks. However, most of the existing summarization models take in the whole document all…
Flexible and Adaptable Summarization via Expertise Separation
Xiuying Chen, Mingzhe Li, Shen Gao +5
A proficient summarization model should exhibit both flexibility -- the capacity to handle a range of in-domain summarization tasks, and adaptability -- the competence to acquire n…
Shadow and Light: Digitally Reconstructed Radiographs for Disease Classification
Benjamin Hou, Qingqing Zhu, Tejas Sudarshan Mathai +3
In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises of 50,188 frontal Digitally…
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…
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
Qingqing Zhu, Xiuying Chen, Qiao Jin +6
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics,…
Rethinking Scientific Summarization Evaluation: Grounding Explainable Metrics on Facet-aware Benchmark
Xiuying Chen, Tairan Wang, Qingqing Zhu +5
The summarization capabilities of pretrained and large language models (LLMs) have been widely validated in general areas, but their use in scientific corpus, which involves comple…