Research

AI Healthcare Research

The AI CoLab advances applied AI research that improves clinical care, strengthens health systems, and promotes health equity. Grounded in the combined resources of MedStar Health and Georgetown University, including access to 1 of the largest real-world EHR systems in the mid-Atlantic region, our work integrates clinical data, machine learning, and digital health technologies across an interdisciplinary team of researchers, data scientists, and clinician investigators. We develop and evaluate AI methods that are clinically meaningful, methodologically rigorous, responsibly grounded, and scalable within real healthcare environments.

Research Focus Areas

• Clinical Decision Support and Predictive Modeling: We build and validate machine learning models that support clinicians across specialties.
• Digital Twins and Remote Monitoring: We develop digital twin frameworks that capture longitudinal patient data to support proactive, personalized care
• Natural Language Processing and Multimodal Data: We apply NLP and multimodal AI methods to extract from free-text clinical notes, imaging, and structured EHR data.
• AI Validation: We design and execute rigorous validation frameworks for AI tools including regulatory readiness and real-world evidence generation.
• Privacy Preserving Data Infrastructure: We develop federated learning and secure data sharing architectures that enable collaborative research without compromising patient privacy.
• Responsible AI: We center responsible and transparent AI across the full research lifecycle, studying how AI tools perform and building AI systems designed to improve healthcare for all.

Funded by

Explore AI CoLab Work

Technology: Voice assisted, digital Twin, remote monitoring

Machine learning to identify heart risks in pregnant patients

AI applications using Electronic Health Records (EHR)

Natural Language Processing (NLP) on free-text data

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