Innovating Medical AI with Purpose

At the IAMAI Center, we focus on pioneering AI methodologies that bring clarity to complex medical challenges. Our research spans critical domains such as multi-modality data integration, longitudinal patient modeling, vascular mapping, and federated learning—all designed to enhance medical understanding, preserve privacy, and improve patient outcomes. Through this work, we’re shaping a future where artificial intelligence is a trusted partner in the diagnosis, treatment, and transformation of healthcare.

Multi-Modality AI in Healthcare

Unifying Data Streams to Deepen Patient Understanding
This research focuses on integrating diverse data types—like images, text, and genomics—using AI to generate a more complete picture of patient health. It addresses the challenges of complexity, misalignment, and bias to support safer, more effective medical decision-making.

Longitudinal Prediction

Mapping Health Trajectories Over Time
By analyzing data across the full span of a patient’s healthcare journey, this research enables AI to predict disease progression, treatment response, and long-term outcomes. It bridges gaps in current models that rely heavily on cross-sectional data.

Vascular Atlas

Understanding the Body’s Vascular Blueprint
This work involves creating detailed maps of the body’s vascular system to study blood flow and nutrient delivery. It supports detection of structural changes across diseases like cancer and inflammation, enabling early identification of vascular abnormalities.

Federated Learning and Distributed Processing in Healthcare

Collaborative AI That Protects Patient Privacy
This research builds frameworks that allow AI models to be trained on distributed datasets across institutions—without sharing sensitive patient data. It enables privacy-preserving, collaborative innovation in medical AI.