Research

Humans have accumulated vast amounts of generational knowledge about how to live a healthy life across the lifespan. Women, as guardians of the mitochrondrial DNA or mtDNA, are the conduit to the health of the next generation. VIHAR  has a compelling research vision of creating the world’s first female digital health twin using advanced AI technologies. The critical need for integrative intelligence combining man-machine systems to perform complex team science will be enabled through VIHAR collaborative research as “co-labs”.

VIHAR VISION

To become the world’s premier innovation center of knowledge and practice in evidence-based girls and women’s integrated health and wellness across the life span.

Digital twin for women-centric health topics

•Modeling female-specific health organs and their functioning in-silico
•Simulated data generation for health across female lifespan from real world data
•Real-time data collection frameworks for female digital twin development

Multimodal LLMs and applications

•Clinical applications of conversational AI agents and ambient intelligence adapted to female-specific mental and physical health examinations
•Combining text with speech and vision for multimodal intelligence

 

Collective Intelligence

•Biomarker discovery using multi-omics for precision medicine and nutrition
•Evidence combination from multiple sources of machines and humans for decision-support
 

Integrative Modeling of woman’s health data across lifecycle  from existing studies

  • Epidemiological data
  • Biomarker data from heterogenous studies and multi-omics
  • Patient-oriented outcomes (PCOR)
  • Wearables or sensor-based data/IoT
  • Genotype – Phenotype for diseases that affect women such as post-partum hemorrhage

ENACT Collaboration with CCAI

Project ENACT and PPH Collaborations:

In collaboration with the Center for Clinical AI and the University of Pittsburgh Family Medicine Department Faculty, VIHAR has started collaborating on generating trends for postpartum hemorrhage occurrence and risk-factors using the ENACT federated electronic health record data. Researchers involved include: 

Shyam Visweswaran, MD, PhD; Olga Kravchenko, PhD; and team: see https://pmc.ncbi.nlm.nih.gov/articles/PMC11469370/