Developing AI tools that seamlessly integrate into clinical workflows to enhance diagnosis, treatment decisions, and patient outcomes through predictive modeling and intelligent decision support.
Our clinical AI research spans the full spectrum of healthcare applications, from early prediction of clinical events to real-time decision support systems that assist clinicians at the point of care.
We focus on developing robust, interpretable AI systems that can handle the complexity and variability of real-world clinical data while maintaining the trust and confidence of healthcare providers.
Our multidisciplinary approach to clinical AI innovation
Advanced machine learning models that predict clinical events, patient outcomes, and disease progression using longitudinal electronic health record data.
Intelligent systems that provide evidence-based recommendations to clinicians at the point of care, integrated seamlessly into existing clinical workflows.
We trust our doctors to have patients' best interests foremost in guiding their decisions. How can we trust AI?
Helped build the national infrastructure for the Undiagnosed Disease Network with 'omic diagnoses at scale'
Ensuring AI systems are robust, reliable, and safe for clinical deployment through rigorous testing and adversarial analysis.
Translating research innovations into practical clinical tools that can be deployed in real healthcare settings with measurable impact on patient care.
Influenced the implementation and design of clinical AI
How do we go from monolithic EHR applications to a flexible open market app economy. This publication led to the definition of the SMART-on-FHIR API that was adopted by most EHR vendors and Apple Health.
Comprehensive editorial in NEJM that launched the NEJM AI journal and provided a roadmap for the future of artificial intelligence applications in clinical medicine.
Identifying vulnerabilities in medical AI systems and establishing frameworks for ensuring robustness and safety in clinical AI deployments.
The context under which data is obtained strongly influences its actionability and interpretation.
Interested in pushing the boundaries of AI in healthcare? Explore opportunities to collaborate with our team or learn more about our ongoing research.