Magma: A Foundation Model for Multimodal AI Agents.

Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale.

BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once.

Training Small Multimodal Models to Bridge Biomedical Competency Gap: A Case Study in Radiology Imaging.

Foundation Models for Biomedical Image Segmentation: A Survey.

Fine-tuning large neural language models for biomedical natural language processing.

Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision.

BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys.

Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events.

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing.

Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning.

Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking.

Clinical trial cohort selection based on multi-level rule-based natural language processing system.