Trustworthy Machine Learning for Healthcare Workshop


Machine learning (ML) has achieved or even exceeded human performance in many healthcare tasks, owing to the fast development of ML techniques and the growing scale of medical data. However, ML techniques are still far from being widely applied in practice. Real-world scenarios are far more complex, and ML is often faced with challenges in its trustworthiness such as lack of explainability, generalization, fairness, privacy, etc. Improving the credibility of machine learning is hence of great importance to enhance the trust and confidence of doctors and patients in using the related techniques. We aim to bring together researchers from interdisciplinary fields, including but not limited to machine learning, clinical research, and medical imaging, etc., to provide different perspectives on how to develop trustworthy ML algorithms to accelerate the landing of ML in healthcare.

Scope and Topics

Interested topics will include, but not be limited to:

The goal of this workshop is to bring together expertise from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability. The challenges to ML come from diverse perspectives in practice, and it is therefore of great importance to establish such an interdisciplinary platform to encourage sharing and discussion of ideas, implementation, data, labelling, benchmarks, experience, etc, and jointly advance the frontiers of trustworthy ML in healthcare.

Tentatively, the workshop will be hosted virtually

Important Dates

Paper Submission Deadline: February 10, 2023 

Extended Paper Submission Deadline: February 20, 2023 (11:59 PM UTC-0)

Decision Notification Date: March 3, 2023 March 7, 2023

Workshop Date:  May 4, 2023

An official workshop proceeding will be published in the LNCS (Lecture Notes in Computer Science) series of Springer

Camera-ready Deadline:  May 20, 2023

IEEE J-BHI Special Issues

A special issue on the topic of trustworthy machine learning for health informatics is organized at the top-tier IEEE Journal of Biomedical and Health Informatics (IEEE J-BHI). Welcome to submit your extended versions!

More information is available:

Deadline for Submission: September 1, 2023 

First Reviews Due: November 1, 2023 

Revised Manuscript Due: January 1, 2024 

Final Decision: March 1, 2024


Please contact us at if you have questions regarding the workshop.

Support Organizations

MICCAI Society Endorsed Event
An official workshop proceeding will be published in the LNCS (Lecture Notes in Computer Science) series of Springer.

Center for Medical Imaging and Analysis, HKUST.