Conference on Health, Inference, and Learning (CHIL) 2026

Elizabeth Healey, Jason Fries, Tom Pollard, Shengpu Tang, Anna Zink, Tom Hartvigsen, Monica Agrawal, Sam Finlayson, Benjamin Glicksberg, Brett Beaulieu-Jones, Kai Wang, Daseyra Fontalvo, Tasmie Sarker, Irene Chen, Emily Alsentzer
Proceedings of the 7th Conference on Health, Inference, and Learning, PMLR 333:1-9, 2026.

Abstract

The Conference on Health, Inference, and Learning (CHIL) focuses on advancing machine learning for health, bringing together clinicians and researchers, across both industry and academia. Since 2022, CHIL has been an official conference of the Association for Health Learning and Inference (AHLI). This volume contains proceedings of the seventh annual CHIL conference, held at the Seattle Children’s Research Institute in the US.

Cite this Paper


BibTeX
@InProceedings{pmlr-v333-healey26a, title = {Conference on Health, Inference, and Learning (CHIL) 2026}, author = {Healey, Elizabeth and Fries, Jason and Pollard, Tom and Tang, Shengpu and Zink, Anna and Hartvigsen, Tom and Agrawal, Monica and Finlayson, Sam and Glicksberg, Benjamin and Beaulieu-Jones, Brett and Wang, Kai and Fontalvo, Daseyra and Sarker, Tasmie and Chen, Irene and Alsentzer, Emily}, booktitle = {Proceedings of the 7th Conference on Health, Inference, and Learning}, pages = {1--9}, year = {2026}, editor = {Healey, Elizabeth and Fries, Jason and Pollard, Tom and Tang, Shengpu and Zink, Anna and Hartvigsen, Tom and Agrawal, Monica and Finlayson, Sam and Glicksberg, Benjamin and Beaulieu-Jones, Brett and Wang, Kai and Fontalvo, Daseyra and Sarker, Tasmie and Chen, Irene and Alsentzer, Emily}, volume = {333}, series = {Proceedings of Machine Learning Research}, month = {29--30 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v333/main/assets/healey26a/healey26a.pdf}, url = {https://proceedings.mlr.press/v333/healey26a.html}, abstract = {The Conference on Health, Inference, and Learning (CHIL) focuses on advancing machine learning for health, bringing together clinicians and researchers, across both industry and academia. Since 2022, CHIL has been an official conference of the Association for Health Learning and Inference (AHLI). This volume contains proceedings of the seventh annual CHIL conference, held at the Seattle Children’s Research Institute in the US.} }
Endnote
%0 Conference Paper %T Conference on Health, Inference, and Learning (CHIL) 2026 %A Elizabeth Healey %A Jason Fries %A Tom Pollard %A Shengpu Tang %A Anna Zink %A Tom Hartvigsen %A Monica Agrawal %A Sam Finlayson %A Benjamin Glicksberg %A Brett Beaulieu-Jones %A Kai Wang %A Daseyra Fontalvo %A Tasmie Sarker %A Irene Chen %A Emily Alsentzer %B Proceedings of the 7th Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2026 %E Elizabeth Healey %E Jason Fries %E Tom Pollard %E Shengpu Tang %E Anna Zink %E Tom Hartvigsen %E Monica Agrawal %E Sam Finlayson %E Benjamin Glicksberg %E Brett Beaulieu-Jones %E Kai Wang %E Daseyra Fontalvo %E Tasmie Sarker %E Irene Chen %E Emily Alsentzer %F pmlr-v333-healey26a %I PMLR %P 1--9 %U https://proceedings.mlr.press/v333/healey26a.html %V 333 %X The Conference on Health, Inference, and Learning (CHIL) focuses on advancing machine learning for health, bringing together clinicians and researchers, across both industry and academia. Since 2022, CHIL has been an official conference of the Association for Health Learning and Inference (AHLI). This volume contains proceedings of the seventh annual CHIL conference, held at the Seattle Children’s Research Institute in the US.
APA
Healey, E., Fries, J., Pollard, T., Tang, S., Zink, A., Hartvigsen, T., Agrawal, M., Finlayson, S., Glicksberg, B., Beaulieu-Jones, B., Wang, K., Fontalvo, D., Sarker, T., Chen, I. & Alsentzer, E.. (2026). Conference on Health, Inference, and Learning (CHIL) 2026. Proceedings of the 7th Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 333:1-9 Available from https://proceedings.mlr.press/v333/healey26a.html.

Related Material