Center for AI and Biomedical Informatics (CAB)
About the Center
We are strategically positioned to drive academic research, enhance competitiveness for extramural funding, and foster multidisciplinary collaborations in the dynamic fields of biomedical informatics and artificial intelligence.
Our in-depth knowledge of and strong partnerships with health plans, care delivery systems, public health agencies, health information exchanges, and other health data aggregators allows access to large datasets and to testing of health informatics and AI innovations to improve population health outcomes.
We will achieve our mission through collaboration, demonstration, innovation, and evidence-generation using state-of-the-art methods and technologies.
Research
Research Areas
Promoting efficiency and safety in healthcare by using AI and LLMs at the population level to generate actionable insights from data. This includes addressing biases in data collection and analysis to improve the accuracy and fairness of these solutions.
Ensuring the accuracy, completeness, and reliability of data used in biomedical informatics to support high-quality research and clinical decision-making. This involves developing, testing and implementing methodologies for data harmonization, validation, cleansing, and monitoring.
Reviewing, testing and promoting standards that enable seamless data exchange and integration across different healthcare systems and platforms. Data standards help to ensure that data can be safely shared and reliably utilized to improve patient care and research outcomes.
Integrating non-medical drivers of health (NMDOH) data into existing clinical and biomedical datasets to better understand and address factors influencing personal and public health outcomes. This research aims to create comprehensive models that incorporate the full scope of a patient’s wellness, including social, economic, and environmental stressors and needs.
Designing and implementing systems that provide personalized, evidence-based recommendations to support patient-centered care. These systems leverage biomedical informatics and AI to integrate patient preferences, clinical guidelines, and real-time data for clinical decision-making.
Exploring the use of decentralized technologies and self-sovereign identity to enhance data privacy and security in healthcare. These projects explore the importance and potential impact of allowing individuals to control their own health data and share it securely.
Research Projects
Our Team
Join our team!
We embody a wide collection of talents, expertise, and backgrounds, cultivating a dynamic and collaborative environment.
CAB Seminars
CAB invites experts to speak at events quarterly. While some events are internal meetings, many will be open to the public.
CAB Updates
Anjum Khurshid spoke at the Health Information Management Society in Las Vegas, NV, participating in panels on "AI and Health Equity" and "Public Health Data Innovations: Interoperability, Real-World Data, and Gen AI."
Anjum Khurshid delivered his keynote address “Empowering communities to share, link, and analyze social service data for collective impact.” at the 8th Global Public Health Conference in Bangkok, Thailand.
Anjum Khurshid served as a panelist on “Data Sharing and Collaboration: Accelerating AI Research" at the AI Revolution in Health Conference in Dubai, United Arab Emirates.