AICARD - Transforming Cardiac Research

Since the emergence of generative AI models like ChatGPT, artificial intelligence has become central to both public discourse and scientific exploration. However, translating these technological advances into clinical practice, where patient safety, clinical outcomes, and clinician trust are paramount, remains challenging. This 'model-to-bedside' bottleneck arises from several interconnected barriers, including fragmented clinical data stored in isolated hospital databases, strict privacy regulations that limit data integration and secure computation, and insufficient transparency and interpretability of AI-driven predictions. Consequently, most AI models remain confined to research settings due to inadequate validation, limited practical usability, and low clinician acceptance. These challenges are particularly pronounced in cardiology, a specialty with diverse multi-modal data including detailed clinical records, numerous cardiac imaging modalities, electrophysiological data, and genetic information.

AICARD aims to overcome these barriers by combining clinical expertise with cutting-edge machine learning and intuitive visualization to create a platform built on three core components:

• Multi-Modal Data Lake
  We will establish a secure, multi-modal data lake that integrates clinical data from diverse sources to effectively eliminate existing data silos.
• Exploratory Visual Data Analysis
  We will make the data lake accessible to different stakeholders through a visual analysis interface as a central hub for exploring the data lake, visualizing patient trajectories, discovering novel phenotypes, and facilitating the interpretation of AI predictions.
• Multi-Modal AI Models
  Utilizing the integrated data lake, we will develop multi-modal AI models to explore and identify novel relationships between patient characteristics and disease progression in real-world medical data. These models will serve as the foundation for accurately predicting key clinical outcomes, such as hospital readmissions, disease progression, and cardiovascular mortality.

Our interdisciplinary approach will strengthen clinician and patient trust in AI models, facilitate their integration into routine clinical practice, and thus substantially improve clinical outcomes.