Webinar #2: Scaling AI to Develop Robust Applications in Medical Imaging - Prof. Daniel L. Rubin скачать в хорошем качестве

Webinar #2: Scaling AI to Develop Robust Applications in Medical Imaging - Prof. Daniel L. Rubin

The IEEE EMBS Webinar Series on Frontiers of Biomedical Imaging and Analysis Webinar #2: Scaling AI to Develop Robust Applications in Medical Imaging There are many exciting prospects of #AI for applications in #medical #imaging, including image enhancement, automated disease detection, diagnosis, and clinical prediction. However, there are several major challenges to be addressed in order to develop robust and clinically useful AI models. First, training robust AI models requires tremendous amounts of labeled data, and while there are abundant images in the historical clinical archives of healthcare institutions, it is difficult to label the images in large scale, which we address through deep learning methods with clinical texts. Second, though federated learning is promising to access multi-institutional data for training AI models, variability in data across hospitals degrades performance of AI models trained this way. Third, it is challenging to evaluate the effectiveness of AI in actual clinical practice. In this talk I will highlight some of the exciting frontiers in AI in medical imaging and the implications for data-driven medicine, focusing on (1) upstream and downstream applications for AI in medical imaging, (2) the challenge of data variability to federated learning and ways to overcome that challenge, (3) infrastructure needed to enable evaluation of AI in the clinical workflow to ensure it improves clinical care as expected.