December 10, 2023
08:00 - 18:00
InterContinental New Orleans

Attendee Feedback

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


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 Medicine 

  • Monica Agrawal, Duke University

How to Responsibly Pilot Generative AI Applications in Health and Medicine: Ethics, Frameworks, Implementation Guidelines, Use Cases 

  • 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 Lialin 

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 Vukadinovic 

How can machine learning promote fairness and enhance global health outcomes?

Senior Chairs: George Chen, Sanjat Kanjilal | Junior Chairs: Vincent Jeanselme 

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 Demirel 

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




We are grateful to our sponsors for making ML4H 2023 a resounding success!

Previous Conference on Health, Inference, and Learning