Maria Zuluaga is an assistant professor in the Data Science department at EURECOM, France. Additionally Maria holds a junior chair at the 3IA Institute Côte d’Azur and also a visiting Senior Lecturer at King’s College London. She focuses on machine learning techniques that can be safely deployed in high risk domains, such as healthcare, by addressing data complexity, low tolerance to errors and poor reproducibility.
D'autres épisodes de "AI-ready Healthcare"
Joe Lennerz: Berlin Declaration of Health Data Sharing
49:37Prof. Jochen Lennerz is the Medical Director of the Center for Integrated Diagnostics at the Massachusetts General Hospital, USA. He is a board-certified pathologist by training and has professorship appointments at Harvard medical School. Prof. Lennerz co-organized the Data4Health 2023 conference in Berlin with the health minister of Germany Prof. Karl Lauterbach. Data4Health 2023 Berlin
Neel Dey: Invariances and Covariances of Medical Imaging
44:08Neel Dey is a postdoctoral researcher at MIT CSAIL in Polina Golland’s Medical Vision Group, where he is building dense representation learning and domain randomization methods for data and compute-efficient learning tasks. Neel got his Ph.D. from New York University under Guido Gerig where he worked on generative models and inverse problems in medical image analysis. E(3) x SO(3) - Equivariant Networks for Spherical Deconvolution in Diffusion MRI AnyStar: Domain randomized universal star-convex 3D instance segmentation
Maria Zuluaga: Trustworthy Medical AI
42:33Maria Zuluaga is an assistant professor in the Data Science department at EURECOM, France. Additionally Maria holds a junior chair at the 3IA Institute Côte d’Azur and also a visiting Senior Lecturer at King’s College London. She focuses on machine learning techniques that can be safely deployed in high risk domains, such as healthcare, by addressing data complexity, low tolerance to errors and poor reproducibility. From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Pieter De Backer: AI-assisted Surgical Training
51:53Pieter De Backer leads the Innotech department at Orsi, a training and innovation centre in minimal invasive & robotic surgery located in Gent, Belgium. Pieter's team focuses primarily on developing AI based surgical systems and patient-specific 3D modeling.
Camila Gonzalez: Medical Continual Learning
53:16Camila Gonzalez is a PostDoc in Stanford University, USA. Camila finished her PhD on medical continual learning in TU Darmstadt in March 2023, while accumulating multiple awards along the process. She is also the outgoing president of MICCAI Student Board, presiding it for the last 2 years. Lifelong nnU-Net: a framework for standardized medical continual learning
Shek Azizi: Google DeepMind's Foundational Medical Models
49:41Shek Azizi is a senior research scientist at Google DeepMind. Her research is focused on translational AI with tangible clinical impact. She designs foundation models for biomedical applications. She has led the moonshot project behind Med-PaLM, Med-PaLM 2 and Med-PaLM M. Large Language Models Encode Clinical Knowledge
Dan Hashimoto: Making surgery AI-ready
52:55Daniel Hashimoto is an assistant Professor of Surgery at the Hospital of the University of Pennsylvania, USA. Dan has developed multiple computer vision algorithms for the analysis of surgical video, led international consensus on defining ground truth for the annotation of surgical video, and worked to define metrics to assess performance of AI algorithms on surgical tasks. His work has been published in the New England Journal of Medicine, Nature Biotechnology, Annals of Surgery, and other journals. He is editor of the textbook Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice. He is also heavily involved in MICCAI society with a focused attention to CLINICCAI.
Stephen Gilbert: AIaMD regulations
1:16:16Prof. Stephen Gilbert is a professor in Medical Device Regulatory Science at the Else Kröner Fresenius Center for Digital Health in TU Dresden, Germany. His research goal is to advance regulatory requirements, especially for software as a medical device and artificial intelligence in medical devices. Papers we discussed: Large language model AI chatbots require approval as medical devices Continuous Improvement of Digital Health Applications Linked to Real-World Performance Monitoring: Safe Moving Targets?
Nitika Pai: Global Digital Health
57:36Prof. Nitika Pai is an Associate Professor in the Department of Medicine at the McGill University, Canada. Her global implementation research program in Canada, India and South Africa is primarily focused on point-of-care diagnostics for HIV and associated co-infections. Her research informs domestic and global policy on point-of-care diagnostics.