About

Multimodal Large Language Models in Clinical Practice

Welcome to the 1st Workshop on MLLMs in Clinical Practice co-located with MICCAI 2025!

Recent advancements in medical multimodal large language models (MLLMs), such as MedGemini, have introduced a transformative era in clinical AI, enabling the integration of various data modalities like 2D/3D medical images, text, and DNA sequences for more comprehensive diagnostics and personalized care. While these models show promise, challenges such as data scarcity, privacy concerns, and the need for more comprehensive evaluation metrics beyond accuracy must be addressed to fully realize their potential. MLLMs also offer exciting opportunities for enhanced human-AI collaboration in clinical workflows, improving diagnostic accuracy and decision-making. To facilitate research in this emerging field, we propose a workshop to foster discussion and collaboration on MLLM development and address the challenges of leveraging these models in clinical practice. The workshop theme includes topics but not limited to dataset construction, safety, fairness, human-AI collaboration, and new evaluation metrics for clinical MLLMs.

Topics of interests include but not limited to:

  • Multimodal Large Language Models (MLLMs) for Healthcare
  • Large-scale Dataset Construction
  • Evaluation Metrics for MLLMs
  • Safety, fairness, and Risks in Deployment
  • Human-AI Collaboration in Diagnosis and Treatment

Workshop Schedule

Program

September 23, 2025 or September 27, 2025 - South Korea (Daejeon) local time (GMT+9)

  • 08:50 - 09:00 | Opening Remarks
  • 09:00 - 09:30 | Invited Talk I by Professor Marinka Zitnik
  • 09:30 - 10:00 | Invited Talk II by Dr Shekoofeh Azizi
  • 10:00 - 10:30 | Contributed Talks I (2)
  • 10:30 - 11:00 | Coffee Break
  • 11:00 - 11:30 | Invited Talk III by Dr Hoifung Poon
  • 11:30 - 12:30 | Poster Session I
  • 12:30 - 13:30 | Lunch Break
  • 13:30 - 14:00 | Invited Talk IV by Professor Hao Chen
  • 14:00 - 14:30 | Contributed Talks II (2)
  • 14:30 - 15:30 | Poster Session I
  • 15:30 - 16:00 | Coffee Break
  • 16:00 - 17:00 | Panel Discussion
  • 17:00 - 17:30 | Invited Talk V by Dr Valentina Salvatelli
  • 17:30 - 17:40 | Closing Remarks

Calls

Call for Papers

We invite original, high-quality research papers on MLLMs in healthcare. Papers should provide comprehensive analysis, robust results, and significant contributions. Previously published or concurrently submitted work will not be considered.

Submission Instructions

Formatting Guidelines:
Submissions are limited to 8 pages (plus 2 pages for references) and must follow MICCAI guidelines.

Submit your paper:



Review Process

All submissions will undergo a double-blind review via Microsoft CMT, with at least three reviewers per paper. Authors are responsible for anonymizing the submissions.


Important Dates

  • For all papers:
    • Submission Deadline: June 25, 2025
    • Notification of Acceptance: July 16, 2025

Highlights

Invited Speakers

Marinka Zitnit

Harvard University

Shekoofeh Azizi

Google DeepMind

Hoifung Poon

Microsoft Health Futures

Hao Chen

Hong Kong University of Science and Technology

Valentina Salvatelli

Microsoft Health Futures

Highlights

Invited Panelists

Jeya Maria Jose

Microsoft Research

Pranav Rajpurkar

Harvard University

Yueming Jin

National University of Singapore

Luping Zhou

University of Sydney

Edward Choi

KAIST

Tanveer Syeda-Mahmood

IBM Research

Organization

Organizing Committee

Yunsoo Kim

University College London

Chaoyi Wu

Shanghai Jiao Tong University

Justin Xu

University of Oxford

Hyewon Jeong

Massachusetts Institute of Technology

Sophie Ostmeier

Stanford University

Michelle Li

Harvard University

Zhihong Chen

Stanford University

Xiaoqing Guo

Hong Kong Baptist University

Yuyin Zhou

University of California, Santa Cruz

Weidi Xie

Shanghai Jiao Tong University

Honghan Wu

University of Glasgow

Curtis Langlotz

Stanford University

Program Committee

  • TBA

Contact us

Email the organziers at: clinicalmllms [at] gmail [dot] com