The goal of Stream 1 is to develop physics- and artificial intelligence-based methods to reduce and simplify scan times, increase the number of quantifiable parameters, and improve disease characterization, ensuring reproducibility across different equipment and medical centers. We aim to overcome current limitations in medical imaging—such as the lack of comprehensive quantification, integration with other clinical data, and high operational costs—through the development of innovative techniques that integrate AI, imaging physics, and multiparametric data, from acquisition to clinical diagnosis.
Research
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Stream 1: Intelligent fully quantitative medical imaging
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Stream 2: AI-enabled physics-informed and synergistic medical imaging
The goal of Stream 2 is to develop physics-informed AI methods that leverage synergies across medical imaging modalities (ultrasound, magnetic resonance, etc.) throughout the entire process (from acquisition to diagnosis and prognosis), and between different specifications (conventional and lower-cost medical imaging equipment) to improve efficiency (faster scans/lower radiation dose), effectiveness (capturing a broader spectrum of disease-related processes), and accessibility (solutions for both conventional and lower-cost systems).
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Stream 3: Explainable AI for medical imaging and non-imaging patient data integrated analysis, reporting and diagnosis
The goal of Stream 3 is to develop self-supervised Explainable Artificial Intelligence (XAI) methods that integrate medical imaging with other patient data (physiological sensors, clinical reports, risk factors, etc.) to provide accurate, reliable, and interpretable AI-assisted clinical analysis. Developing methods that are more explainable for the end-user would significantly support decision-making and build trust among healthcare professionals.
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Stream 4: Population-based phenotyping for cardiovascular, liver disease and cancer
The goal of Stream 4 is to improve the diagnosis and prognosis of cardiovascular, hepatic, and oncological diseases by translating the technical advances from Streams 1 to 3, as well as by correlating AI-derived imaging phenotypes with clinical descriptors, non-imaging data, and outcomes. We also aim to reduce bias in AI model research due to underrepresentation in available datasets by creating a Chilean medical imaging database (iHEALTH-BB).
