Educational Activity

    1. Self assessment questions

    1. AI-Generated Synthetic Data in Hematology

    1. Post activity questions

About this course

  • Free
  • 3 lessons
  • 0.5 hours of video content

Learning Objectives

Upon successful completion of this activity, you should be better prepared to:

  • Define synthetic data as AI-generated medical data (text, images, tabular) designed to mirror real-world datasets while preserving privacy

  • Identify healthcare challenges synthetic data addresses, such as data-sharing restrictions, costly trials, and limited patient diversity

  • Explore applications in cancer and hematology research, including model training, synthetic patient cohorts, and privacy-preserving collaboration

  • Assess technological, ethical, and regulatory considerations, including GANs, data fidelity, bias, privacy risks, and responsible use

Targeted Audience

  • Healthcare professionals

    This educational activity has a primary target audience of oncologists, hematologists, bone marrow transplant experts, researchers, machine learning and data scientists focusing on the application of AI in healthcare

  • AI Enthusiast

    Anyone interested in the application of AI in healthcare.

Presenter

Clinician Scientist, Technical University Dresden Jan-Niklas Eckardt

Dr. Jan-Niklas Eckardt, MD, MSc, MHBA, is a hematologist and oncologist at the University Hospital Carl Gustav Carus Dresden and Co-Group Leader of the “Big Data and Artificial Intelligence in Hematology” research group at TU Dresden. Trained in medicine at Göttingen and clinical research at Dresden and Harvard, he combines clinical expertise with cutting-edge AI to advance diagnostics and personalized care in hematologic malignancies. With more than 50 publications, his work includes deep learning models for leukemia diagnostics and synthetic patient data for clinical research. Beyond academia, he serves as Chief Scientific Officer at Cancilico, a biotech startup focused on AI-driven oncology solutions