Elham Dolatabadi
Assistant Professor & Health AI Scientist at York and Vector Insititute | Advocate for Open-Sourcing Foundation Models and Reproducible Infrastructure in Healthcare
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Elham Dolatabadi, PhD, is an Assistant Professor and early-career researcher at York University. She as an emerging leader in machine learning (ML) for health, an affiliated faculty member at the Vector Institute. Her contributions to date have advanced the development of advanced ML and foundation models across six key areas: clinical diagnostic models, conversational AI, public health surveillance, algorithmic fairness, multimodal learning, and health-sensing technologies
When Foundation Models Go Open: Unlocking the Promise from Infrastructure to Impact in Health
As foundation models become increasingly central to health applications, the open-source community plays a vital role, not only in democratizing access to data and models, but also in driving safe, reproducible, and equitable innovation. In this talk, I will explore the rapidly growing ecosystem of open-source foundation models, with a particular focus on healthcare, where governance, fairness, and privacy are essential.
I will share lessons learned from our team's experience developing and releasing several open-source resources, including datasets, training pipelines, and evaluation frameworks designed to support advanced AI and multimodal learning in health. These tools enable rapid prototyping, benchmarking, and reproducible experimentation across vision-language and other multimodal health tasks.
This session offers a hands-on perspective for researchers and developers looking to engage with open foundation model efforts. Whether you're releasing models, curating datasets, or contributing to tooling, you'll gain insights into best practices for documentation, data quality assurance, and aligning model development with ethical and privacy principles in health AI.
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