2.2k citations · 4k across the 30 of their papers we have counts for
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Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Cliff Wong, Sheng Zhang, Yu Gu +8
Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this pa…
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
Yu Gu, Sheng Zhang, Naoto Usuyama +8
Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs…
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Chunyuan Li, Cliff Wong, Sheng Zhang +6
Conversational generative AI has demonstrated remarkable promise for empowering biomedical practitioners, but current investigations focus on unimodal text. Multimodal conversation…
Self-Verification Improves Few-Shot Clinical Information Extraction
Zelalem Gero, Chandan Singh, Hao Cheng +4
Extracting patient information from unstructured text is a critical task in health decision-support and clinical research. Large language models (LLMs) have shown the potential to…
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization
Griffin Adams, Bichlien H Nguyen, Jake Smith +7
Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address th…
Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making
Aliyah R. Hsu, Yeshwanth Cherapanamjeri, Briton Park +3
Pre-trained transformers are often fine-tuned to aid clinical decision-making using limited clinical notes. Model interpretability is crucial, especially in high-stakes domains lik…
Compositional Zero-Shot Domain Transfer with Text-to-Text Models
Fangyu Liu, Qianchu Liu, Shruthi Bannur +9
Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero…
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
Sheng Zhang, Yanbo Xu, Naoto Usuyama +21
Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process differe…