ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2403.01628
35
2

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium

3 March 2024
Hyewon Jeong
Sarah Jabbour
Yuzhe Yang
Rahul Thapta
Hussein Mozannar
William Jongwon Han
Nikita Mehandru
Michael Wornow
Vladislav Lialin
Xin Liu
Alejandro Lozano
Jiacheng Zhu
Rafal Kocielnik
Keith Harrigian
Haoran Zhang
Edward H. Lee
Milos Vukadinovic
Aparna Balagopalan
Vincent Jeanselme
Katherine Matton
Ilker Demirel
Jason Alan Fries
Parisa Rashidi
Brett K. Beaulieu-Jones
X. Xu
Matthew B. A. McDermott
Tristan Naumann
Monica Agrawal
Marinka Zitnik
Berk Ustun
Edward Choi
Kristen W. Yeom
Gamze Gürsoy
Marzyeh Ghassemi
Emma Pierson
George H. Chen
S. Kanjilal
Michael Oberst
Linying Zhang
Harvineet Singh
Tom Hartvigsen
Helen Zhou
Chinasa T. Okolo
    VLM
    AI4TS
    OOD
ArXivPDFHTML
Abstract

The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant topics for the \ac{ML4H} community. Encouraged by the successful virtual roundtables in the previous year, we organized eleven in-person roundtables and four virtual roundtables at ML4H 2022. The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables. Each roundtable session included invited senior chairs (with substantial experience in the field), junior chairs (responsible for facilitating the discussion), and attendees from diverse backgrounds with interest in the session's topic. Herein we detail the organization process and compile takeaways from these roundtable discussions, including recent advances, applications, and open challenges for each topic. We conclude with a summary and lessons learned across all roundtables. This document serves as a comprehensive review paper, summarizing the recent advancements in machine learning for healthcare as contributed by foremost researchers in the field.

View on arXiv
Comments on this paper