DCDL

Clinical AI in the Real-World: From Data-Centric AI to Dynamic Learning

Palmeraie Conference Centre, Marrakesh

Learn the technical, clinical and regulatory aspects of developing data-centric solutions that evolve over their lifetime in an interactive half-day tutorial taking place in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).

Summary

Recent advances in AI have brought tremendous progress, yet also revealed new challenges around data and model utilization over time. This tutorial examines two emerging and important areas - data-centric AI and continual learning - unpacking issues and state-of-the-art solutions tailored for medical imaging contexts.

In part one, we will cover Data-centric AI which seeks to place the previously undervalued nuances of data at the center of AI development and articulate its transformative potential. We will explore the motivation behind the data-centric approach, highlighting the power to improve model performance, as well as engender more trustworthy, fair, and unbiased AI systems. Our examination extends to standardized documentation frameworks, exposing how they form the backbone of this new paradigm. We will cover state-of-the-art methodologies in (1) data characterization to audit datasets, (2) synthetic data and (3) data-centric AI in the era of Foundation models.

In part two of the tutorial, we move from a static perspective to the dynamic nature of medical AI systems, extending our view to systems that adapt over their lifetime in situations where we have spatial and temporal data availability constraints. We will outline the process of building and deploying federated and continual learning medical AI systems, with a focus on leveraging trained Foundation models. These techniques could extend the lifespan of medical software solutions but also signify technical and regulatory challenges.

Our integrated tutorial will equip participants with a comprehensive understanding and practical skills around two important real-world medical AI challenges. The tutorial aims to provide an interactive and hands-on experience via software tools and interactive coding sessions, thereby enabling practical engagement for participants.

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Speakers

Mihaela van der Schaar

University of Cambridge

Mihaela is the John Humphrey Plummer Professor of ML, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute. Mihaela is also the director of the Cambridge Centre for AI in Medicine. Mihaela's research focus is on ML, AI and operations research for healthcare and medicine. Mihaela has received numerous awards and has significant experience and expertise in bringing complex topics to broad, diverse audiences, including keynotes, video series, and ongoing monthly engagement sessions with both ML researchers and clinicians. Alongside Nabeel Seedat, she hosted a virtual engagement on Data-Centric AI, attended by around 150 participants, and has given talks on data-centric AI to both industry and academic research groups.

Nabeel Seedat

University of Cambridge

Nabeel is an early-stage African researcher (under-represented in the ML community). He is currently a PhD candidate at the University of Cambridge, focusing on Data-Centric AI, uncertainty quantification & synthetic data. He has published papers on Data-Centric AI at leading conferences --- NeurIPS, ICML, ICLR & AISTATS and has given invited talks on Data-Centric AI to both industry: Microsoft Research, AstraZeneca, Discovery Limited and academic research groups: Queen Mary University of London, University of Cape Town. He also has given talks to diverse audiences at conferences including IJCAI, NeurIPS, IEEE, KDD, PyData, Future of Data-Centric AI. He has extensive industry experience in the US and South Africa working on data-centric problems. Nabeel coming from South Africa, received his master's and undergraduate degrees in Biomedical engineering from the University of the Witwatersrand, Johannesburg. Nabeel forms part of the under-represented group of Africans in AI.

Camila González

Stanford University

Camila is a postdoctoral scholar at the Computational Neuroscience Laboratory at Stanford University, where she develops continual learning methods suitable for dynamic settings with ongoing data collection. Last year, she co-organized the first MICCAI tutorial on Dynamic AI in the Clinical Open World. She also presented her translational research at the ContinualAI Society seminar series and helped organize the first ContinualAI Unconference from her roles as Diversity, Equity, and Inclusion (DEI) chair and session chair. Additionally, she participated in an expert panel on Applications of Continual Learning. Her work has received multiple distinctions, including the MICCAI Young Scientist Award, the Francois Erbsmann Award, and the Best Presentation Award at the EuSoMII annual meeting. She has been featured in outlets such as the Computer Vision News magazine and the AI-Ready Healthcare podcast. Outside her research, she presided over the MICCAI student board for two years.