Increasing numbers of Machine Learning-based Software as Medical Devices are approved by organizations such as US FDA, China NMPA, or EU CE among others. As the ML4H field continues to mature and differentiate, there is a growing need for an interface where assumptions prevalent in ML4H research can be validated against the challenges, solutions, and maturity of real-world ML4H tools. The ML4H Demo track aims at submissions that demonstrate real-world applications of ML4H technologies, bridging the gap from proof-of-concept to practical utility.
Accepted submissions will be non-archival and have the opportunity to present their live demo on the day of the event alongside the main poster sessions. Select outstanding demos will be given spotlight talks. Reviewing for the Demo Track will be single blind.
Aug 1st: Submission site opens
Sep 8th AoE: Demo submission deadline
Oct 3rd: Decisions released
Dec 1-2nd: In-person event
Submission Site: https://openreview.net/group?id=ML4H/2025/Demo_Track
Each Demo submission must contain the following two components:
A short writeup (max 2 pages, excluding references) describing the ML4H tool, technology, and application.
LaTeX template: Overleaf
The Spec Sheet should contain the following information:
As reviewing is single blind, the spec sheet should not be anonymized. The spec sheet will only be viewed by the ML4H Demo Review Committee and will not be made public.
A link to a video (at most 2 minutes long) demonstrating the tool in use with a voice-over description. You may assume that the viewer has read the spec sheet prior to watching the video. The video will only be viewed by the ML4H Demo Review Committee and will not be made public. Any PHI or confidential information should be blurred or omitted.
As OpenReview is unable to host large video files, you should first upload the video to a platform such as Dropbox, Google Drive, OneDrive, Vimeo, or YouTube (unlisted is okay), and provide a link to the video in the submission form. Any common video format is acceptable (e.g. MP4, MOV, WMV, AVI). Any submission which does not have a working link to a demo video will be desk-rejected. The demo video will only be used to assess the tool itself, and any fancy editing and VFX (or lack thereof) will not impact the assessment.
Please let us know, however, if there are any extenuating circumstances which prevents you from making and submitting a video.
All submitted demos will be evaluated based on the following selection criteria:
All submissions will undergo a review process by the ML4H Demo Review Committee to uphold the selection criteria and assess the maturity and fit of the submitted demos.
To support a high-quality and equitable review process, AHLI is introducing a new author reviewing policy based on submission volume.
Every submission must nominate one author to review a minimum of three (3) papers. A qualified reviewer will have at least one prior archival publication at a comparable peer-reviewed venue (e.g. a ML for health conference or journal, or a health-focused paper at an ML venue), OR an accepted demo at a previous ML4H event.
If none of the authors meet this qualification, the submission is exempt from this requirement. Please email info@ml4h.cc to request such an exemption. We welcome and encourage submissions from first-time contributors at ML4H. Authors serving as Area Chairs, Senior Area Chairs, or in other organizing roles for ML4H 2025 are exempt from this requirement.
Authors of each submission must nominate at least one reciprocal reviewer at the time of submission. If an author is the nominated reciprocal reviewer for several papers, their reviewing load may increase accordingly.
If the nominated reviewer does not accept their reviewer invitation, their associated submission will be desk rejected. Failure to adequately complete assigned reviews by the rebuttal deadline may also result in desk rejection of all associated submissions. Exceptions may be granted at the discretion of the Program Chairs.
To promote community interaction, at least one presenting author of accepted works must register for the event. Registration details are forthcoming.
Please direct questions to: info@ml4h.cc and follow us on Twitter at @symposiumml4h.