UM-IHC-CA2i/AMPLIFAI
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AI & ML interests
Medical imaging AI, multimodal and foundation models, generative AI, and trustworthy translation of AI into clinical care.
Recent Activity
Papers
AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions
CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model
Organization Card
UM-IHC Center for Applied AI (CA2i)
AI-Enabled Medical Imaging
Advancing safe, trustworthy, and clinically meaningful AI for healthcare.
The Center for Applied AI (CA²i) at the University of Maryland Institute for Health Computing (UM-IHC) brings together computational, clinical, and regulatory expertise to develop and translate AI for healthcare.
This hub highlights AI-Enabled Medical Imaging, an interdisciplinary CA²i program advancing the development, evaluation, and clinical translation of medical imaging AI.
What we work on
- Data infrastructure — medical imaging datasets, multimodal data, and clinical AI benchmarks.
- Clinical AI — foundation and generative models, agentic workflows, quantitative imaging, and sustainable AI.
- Real-world evaluation — clinical validation, human–AI interaction, regulatory science, and post-deployment monitoring.
Featured release
AMPLIFAI
Annotated Multi-Phase Liver Imaging for AI
A multiphase CT dataset and MICCAI 2026 benchmark for developing and evaluating AI for clinical reasoning in LI-RADS assessment of liver lesions.
Dataset → · Paper → · Challenge →
AI-Enabled Medical Imaging · Center for Applied AI (CA2i) at the University of Maryland Institute for Health Computing
Affiliated with the University of Maryland Medical Intelligent Imaging (UM²ii) Center
Co-led by Heng Huang, PhD and Florence X. Doo, MD, MA, CIIP
Affiliated with the University of Maryland Medical Intelligent Imaging (UM²ii) Center
Co-led by Heng Huang, PhD and Florence X. Doo, MD, MA, CIIP
models 0
None public yet