55 citations · 131 across the 5 of their papers we have counts for
6 papers
MedGemma Technical Report
Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri +78
Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks,…
Health AI Developer Foundations
Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick +23
Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel…
Advancing Multimodal Medical Capabilities of Gemini
Lin Yang, Shawn Xu, Andrew Sellergren +44
Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Buil…
Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries
Sahar Kazemzadeh, Jin Yu, Shahar Jamshy +21
Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a bar…
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
Zaid Nabulsi, Andrew Sellergren, Shahar Jamshy +20
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific…
Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging
Ahmet Tuysuzoglu, Jeremy Tan, Kareem Eissa +3
Real-time localization of prostate gland in trans-rectal ultrasound images is a key technology that is required to automate the ultrasound guided prostate biopsy procedures. In thi…