Poon, Hoifung

Domain-specific language model pretraining for biomedical natural language processing

Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 2,170

BioGPT: generative pre-trained transformer for biomedical text generation and mining

Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., & Liu, T.-Y. (2022) · Briefings in Bioinformatics

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 1,150

LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · Advances in Neural Information Processing Systems 36

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 986

A whole-slide foundation model for digital pathology from real-world data

Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 921

LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 590

Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

Boecking, B., Usuyama, N., Bannur, S., Castro, D. C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et al. (2022) · Lecture Notes in Computer Science

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 317

A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs

Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., et al. (2025) · NEJM AI

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 283

Classification of common human diseases derived from shared genetic and environmental determinants

Wang, K., Gaitsch, H., Poon, H., Cox, N. J., & Rzhetsky, A. (2017) · Nature Genetics

Genetic Associations and EpidemiologyChronic Disease Management Strategies

Cited by 242

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Nori, H., Lee, Y. T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., King, N., Larson, J., Li, Y., Liu, W., et al. (2023) · arXiv

Topic ModelingArtificial Intelligence in Healthcare and Education

Cited by 178

BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once

Zhao, T., Gu, Y., Yang, J., Usuyama, N., Lee, H. H., Naumann, T., Gao, J., Crabtree, A., Abel, J., Moung-Wen, C., et al. (2024) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 118

Literome: PubMed-scale genomic knowledge base in the cloud

Poon, H., Quirk, C., DeZiel, C., & Heckerman, D. (2014) · Bioinformatics

Biomedical Text Mining and OntologiesBioinformatics and Genomic Networks

Cited by 73

A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings

Zambrano Chaves, J. M., Huang, S.-C., Xu, Y., Xu, H., Usuyama, N., Zhang, S., Wang, F., Xie, Y., Khademi, M., Yang, Z., et al. (2025) · Nature Communications

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 45

Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology

Wong, C., Zhang, S., Gu, Y., Moung, C., Abel, J., Usuyama, N., Weerasinghe, R., Piening, B., Naumann, T., Bifulco, C., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 28

Self-Verification Improves Few-Shot Clinical Information Extraction

Gero, Z., Singh, C., Cheng, H., Naumann, T., Galley, M., Gao, J., & Poon, H. (2023) · arXiv

Machine Learning in HealthcareTopic Modeling

Cited by 21

Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events

Gu, Y., Zhang, S., Usuyama, N., Woldesenbet, Y., Wong, C., Sanapathi, P., Wei, M., Valluri, N., Strandberg, E., Naumann, T., et al. (2023) · arXiv

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 18

DocLens: Multi-aspect Fine-grained Medical Text Evaluation

Xie, Y., Zhang, S., Cheng, H., Liu, P., Gero, Z., Wong, C., Naumann, T., Poon, H., & Rose, C. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Biomedical Text Mining and OntologiesText Readability and Simplification

Cited by 8

Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries

Gero, Z., Singh, C., Xie, Y., Zhang, S., Subramanian, P., Vozila, P., Naumann, T., Gao, J., & Poon, H. (2024) · arXiv

Topic ModelingAdvanced Text Analysis Techniques

Cited by 5

Generative Medical Event Models Improve with Scale

Waxler, S., Blazek, P., White, D., Sneider, D., Chung, K., Nagarathnam, M., Williams, P., Voeller, H., Wong, K., Swanhorst, M., et al. (2025) · arXiv

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 4

The Illusion of Readiness in Health AI

Gu, Y., Fu, J., Liu, X., Valanarasu, J. M. J., Codella, N. C., Tan, R., Liu, Q., Jin, Y., Zhang, S., Wang, J., et al. (2025) · arXiv

Artificial Intelligence in Healthcare and EducationAdversarial Robustness in Machine Learning

Cited by 1