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About ML4H
The Machine Learning for Health (ML4H) Symposium is dedicated to establishing itself as the leading platform for showcasing and exchanging groundbreaking, interdisciplinary research in the application of machine learning to health. Our ambition is to maintain ML4H’s reputation as a pivotal scientific gathering that unites the community, fostering discussions and insights into the latest advancements in machine learning for health.
The 3rd annual ML4H Symposium took place in person on December 10, 2023, in New Orleans, LA. This year’s event emphasized the critical themes of global health and health equity alongside the innovative developments in generative AI, reflecting our commitment to addressing the most pressing challenges in health through the lens of machine learning.
Program Events
Keynote Speakers
Measuring the Social Determinants of Health with Social Media, Mobile Phone and Image Data
Unlocking Mysteries: AI for Rare Disease Diagnosis and Therapeutic Innovation
Sessions
Using Machine Learning to Increase Equity in Healthcare and Public Health
- Emma Pierson, Cornell Tech
ML+Optimization for Social Impact: Improving Maternal & Child Health Outcomes
- Milind Tambe, Harvard University
Thinking Outside the ML Box
- Charles Delahunt, Global Health Labs
Reflections on LLMs in MedicineÂ
- Monica Agrawal, Duke University
How to Responsibly Pilot Generative AI Applications in Health and Medicine: Ethics, Frameworks, Implementation Guidelines, Use CasesÂ
- Stefan Harrer, Science Digital, CSIRO
Large Language Models in Biomedicine
- Tao Tu, Google Research
Research Roundtables
Senior Chairs: Jason Fries, Parisa Rashidi | Junior Chairs: Hussein Mozannar, Rahul Thapta
Safely integrating AI into healthcare workflows, enhancing clinician-AI partnerships, measuring impact, understanding caregiver needs, and shaping inclusive AI regulation
Senior Chairs: Brett Beaulieu-Jones, Xuhai Orson Xu | Junior Chairs: William Jongwon Ha, Nikita Mehandru
Accelerating AI progress demands developing model-agnostic integration methods for evolving healthcare models within clinical workflows.
Senior Chairs: Matthew McDermott, Tristan Naumann | Junior Chairs: Michael Wornow, Vlad LialinÂ
What are the pros and cons of training foundation models on combined data from multiple sources compared to over-training individual models for each hospital?
Senior Chair: Monica Agarwal | Junior Chairs: Xin Liu, Alejandro Lozano
Exploring the adaptation of generative AI in healthcare to enhance patient care, while addressing data privacy and identifying immediate applications for large language models
Senior Chairs: Marinka Zitnik | Junior Chairs: Jiacheng Zhu, Rafal Dariusz Kocielnik
How can we effectively integrate multiple data sources for ML applications in healthcare? How does this work in real-time in a hospital?
Senior Chairs: Berk Ustun | Junior Chairs: Haoran Zhang, Keith Harrigian
How can we ensure the robustness and generalizability of a ML model?
Senior Chairs: Edward Choi, Kristen Yeomi | Junior Chairs: Edward Lee
What are the key elements in healthcare that can enhance patient access to health AI, and how do they influence AI model development?
Senior Chairs: Marzyeh Ghassemi, Emma Pierson | Junior Chairs: Aparna Balagopalan, Sarah Jabbour
How can we preserve patient privacy and maintain data security while leveraging machine learning techniques in healthcare?
Senior Chairs: Gamze Gürsoy | Junior Chairs: Milos VukadinovicÂ
How can machine learning promote fairness and enhance global health outcomes?
Senior Chairs: George Chen, Sanjat Kanjilal | Junior Chairs: Vincent JeanselmeÂ
Where do we stand with ML’s role in population health? How can ML be applied for time-to-event survival analysis? How ML is aiding in preventing and responding to outbreaks of infectious diseases?
Senior Chairs: Michael Oberst, Linying Zhang | Junior Chairs: Katherine Matton, Ilker DemirelÂ
How can recent advances in AI/ML help discover causal relations using clinical data? To what extent can we use observational data to emulate randomized trials, to evaluate the causal effect of any treatment?
Awards and Recognition
To access these papers and additional conference proceedings, please click the button below
Gallery
Recording
Sponsors
We are grateful to our sponsors for making ML4H 2023 a resounding success!
