Courses / Data-Centric Biomedical AI
Module 3 of 7
Data-Centric Biomedical AI
High-quality data is the foundation of every reliable AI system. This module covers ethical data sourcing, human annotation, FAIR data principles, and governance for secure multi-institutional sharing. You’ll also learn to preprocess biomedical datasets, manage outliers, perform imputation, and build AI/ML-ready data pipelines.
Free
Format · Online
Start date · Aug 31, 2026
Duration · 2 weeks
Time · 5 hrs / week
Microskills · 7
CME credits · 11
What you will gain
Course Outcomes
Integration of AI techniques
Integrate AI and ML into healthcare research and clinical practice.
Real-world data application
Use real medical data and case studies to address healthcare challenges.
Practical AI skills
Work with code-free AI/ML tools — no programming knowledge needed.
Critical thinking
Analyze complex healthcare problems through AI-driven insights and methodologies.
What you will learn
Microskills in this Module
Each module covers 5 AI microskills plus one Scientific Rigor and Reproducibility (SRR) and one Responsible Conduct of Research (RCR) microskill.
- The importance of data for developing biomedical AI
- Acquiring ethically sourced biomedical data
- Understanding the role of human annotation
- Promoting FAIR biomedical data principles
- Developing AI/ML-ready biomedical datasets
- Navigating multi-institutional data sharing challenges
- Secure and ethical use of biomedical data
Meet your instructors
Expert Faculty from the University of Florida
Azra Bihorac MD, MS
Senior Associate Dean for Research Affairs · Director, IC3
Jie Xu, PhD
Associate Professor
Tyler Loftus MD, PhD
Associate Professor of Surgery · Associate Director, IC3
Elizabeth Palmer PhD
Asst. Director of Training & Education
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Ready to enroll?
Free enrollment. No coding required. Two cohorts per year — Spring and Fall.