Area of research
Radiology, Nuclear Medicine and Imaging · Artificial Intelligence
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, Machine Learning in Healthcare, and Artificial Intelligence in Healthcare and Education.
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography
EchoGraph System for Automated Quality Assessment of Echocardiography Reports
A large language model–based generative natural language processing framework fine‐tuned on clinical notes accurately extracts headache frequency from electronic health records
Patient-centric Summarization of Radiology Findings Using Two-step Training of Large Language Models
AI pitfalls and what not to do: mitigating bias in AI
Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research
Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports
“Shortcuts” Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation
AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study
Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care
Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography
AI recognition of patient race in medical imaging: a modelling study
Optimizing risk-based breast cancer screening policies with reinforcement learning
Automatic Classification of Cancer Pathology Reports: A Systematic Review
Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset
A Systematic Review of ‘Fair’ AI Model Development for Image Classification and Prediction
Natural Language Processing Model for Identifying Critical Findings—A Multi-Institutional Study
Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma
Real-time sepsis prediction using fusion of on-chip analog classifier and electronic medical record
Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model
Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records
Patient-specific COVID-19 resource utilization prediction using fusion AI model
Development and Use of Natural Language Processing for Identification of Distant Cancer Recurrence and Sites of Distant Recurrence Using Unstructured Electronic Health Record Data
SCU‐Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms
Fully Integrated Analog Machine Learning Classifier Using Custom Activation Function for Low Resolution Image Classification
Weakly supervised temporal model for prediction of breast cancer distant recurrence
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection
PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging
Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers