MLHC invites submissions to a full, archival Research Track and a non-archival Clinical Abstracts Track. Submissions accepted to both tracks will be presented at the proceedings event; and for both tracks, at least one author is required to attend, should their work be accepted.

Both the Research Track and Clinical Abstracts Track are received through OpenReview:
https://openreview.net/group?id=mlforhc.org/MLHC/2025/Conference

Hence, all submitting authors are required to have active OpenReview profiles by the submission deadlines.

MLHC 2026 TIMELINE
OpenReview account creation deadline: April 3rd, 2026 (If you do not already have an OpenReview account, please register by this date, otherwise it cannot be guaranteed that your account will be activated in time.)

Full submission deadline (Research and Clinical Abstracts Tracks): April 17th, 2026

Review period: April 17th – May 15th, 2026

Author Rebuttal period: June 1st – June 15th, 2026

Reviewer - AC discussion: June 15th – June 26th, 2026

Paper decision notifications: July 3rd, 2026

Conference dates: August 12-14th, 2026


SUBMISSION INFORMATION
For decades, experts in computer science and medical informatics have explored machine learning techniques to harness data in ways that could propel clinical medicine forward. Continued advancement of these techniques, including predictive and generative models for biomedical images and text such as LLMs, together with the growth of digital health technologies (e.g., electronic health records (EHRs), wearable devices, mobile health apps) and the involvement of tech-savvy clinicians, have led to significant, ongoing progress in applying machine learning to healthcare.

Achieving this vision, however, requires overcoming challenges related to processing complex data such as images, sensor data, and multi-modal patient records. It also requires interpreting these data to deliver actionable insights, including support for decision-making and the quantification of causal effects, as well as examining the resulting impact on clinical workflows and healthcare more broadly.

Maximizing the positive impact of machine learning in healthcare requires close collaboration between a diverse set of experts, including not only technical researchers and clinical staff, but also implementation scientists, ethicists, and policy experts. This collaboration is necessary to identify key problems, curate relevant datasets, and validate findings to ensure solutions work effectively in practice. While machine learning has made progress in handling complex data, much more remains to be done, especially in transitioning from predictive and generative models to practical tools that positively impact clinical decision-making and patient welfare.

The Machine Learning for Healthcare Conference (MLHC) serves as a leading venue dedicated to this dynamic intersection. Since its inception in 2016, MLHC has brought together thousands of researchers in machine learning and clinical fields to share pioneering work (archived in the Proceedings of Machine Learning Research), and foster new partnerships.

MLHC’s guiding principle is that accepted papers should provide important new generalizable insights about machine learning in the context of healthcare.

MLHC invites submissions to a full, archival Research Track and a non-archival Clinical Abstracts Track. Submissions accepted to both tracks will be presented at the proceedings event; and for both tracks, at least one author is required to attend, should their work be accepted.

Both the Research Track and Clinical Abstracts Track are received through OpenReview:
https://openreview.net/group?id=mlforhc.org/MLHC/2025/Conference

For the Research Track, all submitting authors are required to have active OpenReview profiles by the submission deadlines. For the Clinical Abstracts Track, at least one author should have an active OpenReview profile.