Qingyu Chen, PhD

Dr. Chen's research focuses on data science and artificial intelligence (AI) in biomedicine and healthcare. He has led significant milestones from data generation to method development and practical applications. His research interests can be broadly categorized into three areas:

• Biomedical Natural Language Processing and Large Language Models
• Medical Image and Multimodal Analysis in Healthcare
• Downstream Accountability and Trustworthy AI for Medical Applications

Dr. Chen is the Principal Investigator of the K99/R00 grant on multimodal AI-assisted disease diagnosis. He has authored over 40 first/last-author papers out of a total of 80+ publications within these research areas. My work has been featured in venues such as Nature, Nature Medicine, Nature Machine Intelligence, Nature Aging, NPJ Digital Medicine, Nucleic Acids Research, among others.

His research has been recognized with several awards, including the NIH Fellows Award for Research Excellence (twice), AI Talent Scholar (Top 50 in AI across disciplines, selected by Baidu Scholar), NLM Honor Award (twice), and top-ranked performances in biomedical and clinical NLP challenges (four times, three as first author).

He has also taught over 20 courses and mentored more than 10 trainees. My teaching and mentoring have been recognized with the NIH Summer Research Mentor Award (four times) and Excellence in Teaching Awards (twice).

Selected recent work

1. Outpatient Reception via Collaboration Between Nurses and a Large Language Model: A Randomized Controlled Trial. Nature Medicine, 2024.
2. GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information. Bioinformatics, 2024.
3. Advancing Entity Recognition in Biomedicine via Instruction Tuning of Large Language Models. Bioinformatics, 2024.
4. Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering. Journal of the American Medical Informatics Association, 2024.
5. Large Language Models in Biomedical Natural Language Processing: Benchmarks, Baselines, and Recommendations. 2023.
6. LitCovid in 2022: An Information Resource for the COVID-19 Literature. Nucleic Acids Research, 2023. (LitCovid has been accessed millions of times per month.)
7. DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity. Ophthalmology, 2022.
8. Detecting Visually Significant Cataract Using Retinal Photograph-Based Deep Learning. Nature Aging, 2022.
9. Predicting Myocardial Infarction Through Retinal Scans and Minimal Personal Information. Nature Machine Intelligence, 2022.
10. Multimodal, Multitask, Multiattention (M3) Deep Learning Detection of Reticular Pseudodrusen: Toward Automated and Accessible Classification of Age-Related Macular Degeneration. Journal of the American Medical Informatics Association, 2021. (NLM Honor Award)
11. LitCovid: An Open Database of COVID-19 Literature. Nucleic Acids Research, 2021.
12. Keep Up with the Latest Coronavirus Research. Nature, 2021.

Large Language Models in Biomedicine: Selected Paper Series of Our Research

• Outpatient Reception via Collaboration Between Nurses and a Large Language Model: A Randomized Controlled Trial. Nature Medicine, 2024.
• Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health. Briefings in Bioinformatics, 2024.
• GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information. Bioinformatics, 2024.
• Advancing Entity Recognition in Biomedicine via Instruction Tuning of Large Language Models. Bioinformatics, 2024.
• Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering. Journal of the American Medical Informatics Association, 2024.
• Large Language Models in Biomedical Natural Language Processing: Benchmarks, Baselines, and Recommendations. 2023.
• Large Language Models and the Retina: A Review of Current Applications and Future Directions. 2023.

Biomedical Foundation Models: Selected Paper Series of Our Research

• Me llama: Foundation large language models for medical applications. 2024.
• Bioformer: an efficient transformer language model for biomedical text mining. 2023.
• MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval. Bioinformatics. 2023.
• BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale. PLoS computational biology. 2020
• BioSentVec: creating sentence embeddings for biomedical texts. IEEE International Conference on Healthcare Informatics. 2019
• BioWordVec, improving biomedical word embeddings with subword information and MeSH. Scientific data. 2019

AI in Healthcare: Selected Paper Series of Our Research

• A deep network DeepOpacityNet for detection of cataracts from color fundus photographs. Nature Communications Medicine. 2023.
• DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity. Ophthalmology, 2022.
• Detecting Visually Significant Cataract Using Retinal Photograph-Based Deep Learning. Nature Aging, 2022.
• Predicting Myocardial Infarction Through Retinal Scans and Minimal Personal Information. Nature Machine Intelligence, 2022.
• Learning Structure from Visual Semantic Features and Radiology Ontology for Lymph Node Classification on MRI, MLMI, 2021.
• Predicting risk of late age-related macular degeneration using deep learning. NPJ digital medicine, 2020.
• DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs, Ophthalmology, 2019.

For more information, please visit https://sites.google.com/view/qingyuchen/home.