
Ellen Grant & Shadi Albarqouni : Collaborative AI and Data Diversity in Precision Medicine
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
-
ナレーター:
-
著者:
このコンテンツについて
Ellen Grant (Harvard Medical School, Boston Children's Hospital) and Shadi Albarqouni (University of Bonn, Germany) Ellen Grant, a pediatric neuroradiologist, discussed her work on the Healthy Brain and Child Development Project (HBCD), which follows 7,200 mother-infant pairs from pregnancy through childhood to understand the environmental and genetic influences on brain development. She emphasized the challenges of obtaining diverse, representative datasets and fostering international collaboration due to regulatory hurdles. Shadi Albarqouni shared insights into his research on federated learning, a collaborative machine learning approach that enables secure data sharing across institutions. His work focuses on developing inclusive, generalizable AI models for medical imaging, particularly in mammography, while addressing population diversity and data privacy. Both experts highlighted the importance of interdisciplinary collaboration and events like this symposium, which bring together diverse fields to build more accurate and comprehensive precision medicine models. They stressed the need for trajectory-based approaches rather than single time-point analyses to better predict complex systems' outcomes.
For more information, please visit : https://www.p2m-symposium.com/
Hosted on Acast. See acast.com/privacy for more information.