The AHLI Conference on Health, Inference, and Learning (CHIL) solicits work across a variety of disciplines at the intersection of machine learning and health. CHIL 2026 invites submissions focused on artificial intelligence and machine learning (AI/ML) techniques that address opportunities and challenges in health, which we view broadly as including clinical healthcare, public health, population health, and beyond.

Specifically, authors are invited to submit 8-10 page papers (with unlimited pages for references) to one of 3 possible tracks: Models and Methods, Applications and Practice, or Impact and Society. Each track is described in detail below. Authors will select exactly one primary track when they register each submission, in addition to one or more sub-disciplines. Appropriate track and sub-discipline selection will ensure that each submission is reviewed by a knowledgeable set of reviewers.

Important Dates
Submissions open: Wednesday, December 10, 2025
Submissions due: Wednesday, February 4, 2026
Bidding opens for reviewers: Early Feb 2026
Bidding closes for reviewers: Early to mid-Feb 2026
Reviews assigned: Monday, February 9, 2026
Reviews due: Monday, March 2, 2026
Reviews released: Tuesday, March 10, 2026
Author/reviewer discussion: Tuesday, March 24, 2026
Meta-review deadline: Tuesday, March 31, 2026
Author notification: Thursday, April 9, 2026
CHIL conference: June 28-30, 2026

Tracks
Track 1: Models and Methods: Algorithms, Inference, and Estimation
Track 2: Applications and Practice: Investigation, Evaluation, Interpretation, and Deployment
Track 3: Impact and Society: Policy, Public Health, Social Outcomes, and Economics
Evaluation
Works submitted to CHIL will be reviewed by at least 3 reviewers. Detailed reviewer instructions and evaluation criteria will be posted later. Reviewers will be asked to primarily judge the work according to the following criteria:

Relevance: Is the submission relevant to health, broadly construed? Does the problem addressed fall into the domains of machine learning and health?

Quality: Is the submission technically sound? Are claims well supported by theoretical analysis or experimental results? Are the authors careful and honest about evaluating both the strengths and weaknesses of their work? Is the work complete rather than a work in progress?

Originality: Are the tasks, methods and results novel? Is it clear how this work differs from previous contributions? Is related work adequately cited to provide context? Does the submission contribute unique data, unique conclusions about existing data, or a unique theoretical or experimental approach?

Clarity: Is the submission clearly written? Is it well-organized? Does it adequately provide enough information for readers to reproduce experiments or results?

Significance: Is the contribution of the work important? Are other researchers or practitioners likely to use the ideas or build on them? Does the work advance the state of the art in a demonstrable way?

Final decisions will be made by Track and Proceedings Chairs, taking into account reviewer comments, ratings of confidence and expertise, and our own editorial judgment. Reviewers will be able to recommend that submissions change tracks or flag submissions for ethical issues, relevance and suitability concerns.