Area of research
Artificial Intelligence · Molecular Biology
Research interest
Research interests include Biomedical Text Mining and Ontologies, Topic Modeling, Natural Language Processing Techniques, and Advanced Text Analysis Techniques.
AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning
Environment scan of generative AI infrastructure for clinical and translational science
AI Workflow, External Validation, and Development in Eye Disease Diagnosis
Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine
Large language models in biomedicine and health: current research landscape and future directions
A survey of recent methods for addressing AI fairness and bias in biomedicine
Closing the gap between open source and commercial large language models for medical evidence summarization
Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study
Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling
Quality of Answers of Generative Large Language Models vs Peer Patients for Interpreting Lab Test Results for Lay Patients: Evaluation Study.
A scoping review on multimodal deep learning in biomedical images and texts
Improving model fairness in image-based computer-aided diagnosis
Utilizing Longitudinal Chest X-Rays and Reports to Pre-fill Radiology Reports
Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII
A deep network DeepOpacityNet for detection of cataracts from color fundus photographs
Predicting myocardial infarction through retinal scans and minimal personal information
DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity
A roadmap for the functional annotation of protein families: a community perspective
Detecting visually significant cataract using retinal photograph-based deep learning
Robust convolutional neural networks against adversarial attacks on medical images
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Recent advances in biomedical literature mining
COVID-19-CT-CXR: A Freely Accessible and Weakly Labeled Chest X-Ray and CT Image Collection on COVID-19 From Biomedical Literature
ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases
GRID: a student project to monitor the transient gamma-ray sky in the multi-messenger astronomy era
Opportunities and obstacles for deep learning in biology and medicine
Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine
BioCreative V CDR task corpus: a resource for chemical disease relation extraction