Empowering Health Care with Transformative AI
At UTHealth Houston, we are developing AI innovations that go beyond research — directly transforming patient care, disease prevention, and biomedical discovery. By integrating machine learning, neural networks, and generative AI research into healthcare, our experts are accelerating medical breakthroughs and improving treatment outcomes for diverse patient populations.
Purpose
Modernizes clinical research data collection and management through AI-enhanced REDCap functionality
Target Users
Clinical Researchers, Research Coordinators, Data Managers, Principal Investigators
Business Value
Accelerates clinical research workflows and improves data quality through AI-powered automation
Purpose:
AI-powered clinical decision support
Target Users:
Pathologists, Surgeons, Clinical Staff
Business Value:
Improves diagnostic accuracy and surgical outcomes
Purpose:
Centralized management and monitoring of federated learning workflows across distributed clients
Target Users:
AI researchers & data scientists, multi-institutional research teams, privacy-focused organizations, distributed ML practitioners, healthcare research consortia
Business Value:
Enables scalable, privacy-preserving collaborative AI model training across multiple institutions with comprehensive monitoring and management capabilities
Purpose:
Standardizes clinical phenotype definitions and enables reproducible research across healthcare institutions
Target Users:
Researchers, Bioinformaticians, Clinical Scientists
Business Value:
Facilitates research on computational phenotypes and their downstream applications
Purpose:
Provides intelligent statistical power analysis and sample-size calculations for research studies
Target Users:
Researchers, Biostatisticians, Clinical Investigators
Business Value:
Improves study design and statistical rigor, reducing study costs
