Medical Journals

• Wang et al., GeneAgent: Self-verification language agent for gene set analysis using domain databases Nature Methods, to appear.
• Chen et al., Benchmarking LLMs for biomedical NLP applications and recommendations Nature Communications, In Press
• Jin et al., Matching patients to clinical trials with large language models. Nature Communications, 2024
• Allot et al., Tracking genetic variants in the biomedical literature using LitVar 2.0. Nature Genetics, 2023
• Lin et al., Improving model fairness in image-based computer-aided diagnosis Nature Communications, 2023
• Tram et al., Detecting visually significant cataract using retinal photograph-based deep learning. Nature Aging, 2022
• Diaz-Pinto et al., Predicting myocardial infarction through retinal scans and minimal personal information Nat Mach Intell, 2022
• Chen et al., LitCovid: Keep up with the latest coronavirus research Nature, 2020
• Peng et al., Predicting risk of late age-related macular degeneration using deep learning npj Digital Medicine, 2020
• Leaman et al., Ten tips for a text-mining-ready article: How to improve discoverability and interpretability PLoS Biology, 2020
• Peng et al., DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs Ophthalmology, 2019
• Fiorini et al., How user intelligence is improving PubMed. Nature Biotechnology, 2018
• Fiorini et al., Best Match: new relevance search for PubMed. PLoS Biology, 2018
• Fiorini et al., Towards PubMed 2.0 Elife, 2017
• Wei et al., PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res., 2013

CS Conferences

• Khandekar et al., Evaluating Large Language Models for Medical Calculations NeurIPs (Oral), 2024
• Xiong et al., Benchmarking Retrieval-Augmented Generation for Medicine ACL (Findings), 2024
• Jin et al., LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search SIGIR, 2023
• Yan et al., Holistic and comprehensive annotation of clinically significant findings on diverse CT images: learning from radiology reports and label ontology. CVPR, 2019
• Mohan et al., A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval WWW, 2018
• Wang et al., Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays CVPR, 2018
• Shen et al., SetSearch+: Entity-Set-Aware Search and Mining for Scientific Literature. KDD, 2018
• Wang et al., ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases CVPR, 2017