• CMT submission system opens: April 14, 2025
• Paper submission deadline: June 14, 2025
• Notification of acceptance: July 14, 2025
• Camera-ready submission deadline: Sep 8, 2025

*All deadlines expire on 23:59 AoE

• Full papers: Suitable for novel contributions. Must include results of analyses based on real-world data (preferably) or simulated quantiative data. The maximum length of papers is 16 pages (including references) in this format.
• Short papers: Suitable for discussing novel ideas. This can include open research challenges or practical applications to foster discussion among panelists and facilitate future collaborations. Please note that the page limit is 8 pages (including references) in this format.
• Extended Abstracts of already published work: Suitable for discussing previously published related work. Please mention the venue where the work was published. Please note that the page limit is 4 pages (including references) in this format.

In certain cases, authors of full submissions may be asked to revise and submit their work as a short paper.

Each submission should also include a section titled Generalizable Insights about Responsible Application of Machine Learning in Healthcare. For extended abstracts and abstracts of already published works, this section should provide a concise summary of key insights. In contrast, for full papers, a more detailed and comprehensive discussion is expected.

Note: Submissions can include a variety of paper types, such as research, position papers, case studies, experience reports, survey papers, evaluation papers, technical reports, policy papers, methodology papers, and others relevant to the workshop.

Papers must be written in English and formatted in LaTeX, following the outline of the author kit given here: Springer LNCS Template. The kit includes a readme document, a LaTeX file template containing author instructions, and style files. The program chairs reserve the right to reject any over-length papers without review. Papers that ‘cheat’ the page limit by, including but not limited to, using smaller than specified margins or font sizes will also be treated as over-length. Note that, for example, negative vspaces are also not allowed by the formatting guidelines; further details can be found in the author kit. Up to 10 MB of supplementary materials (e.g., proofs, audio, images, video, data, or source code) can be uploaded with your submission. The Program Committee reserves the right to judge the paper solely on the basis of the paper; looking at any supplementary material is at the discretion of the reviewers and is not required.

Submitted papers will be reviewed by the Program Committee in a single-blind manner—that is, authors must include their names in the submissions, and their identities will be visible to the reviewers.

Submissions should be done via the Conference Management Toolkit (CMT) page: Conference Management Toolkit (CMT) page. To submit your manuscript, please follow these steps:

• In the “Submissions” tab, ensure that the selected conference is “ECMLPKDDworkshop2025”.
• Click the “+Create new submission” button, and select “Workshop on Responsible Healthcare using Machine Learning (RHCML)” as the track.

We invite submissions on all aspects of using machine learning for responsible healthcare. Topics of interest include, but are not limited to, the following illustrative areas:

• Fairness and Equity in ML-based Healthcare
  Approaches to detect, mitigate, and prevent bias in ML-based healthcare models to ensure fair and equitable outcomes across diverse patient populations.
• Privacy-Preserving ML in Healthcare
  Techniques such as federated learning, differential privacy, and secure computation to ensure data privacy while leveraging healthcare data for ML models.
• Explainability and Interpretability of Machine Learning in Healthcare
  Methods to make ML models transparent and understandable to healthcare professionals, ensuring trust, accountability, and usability in clinical decision-making.
• Responsible Use of Large Language Models (LLMs) in Healthcare
  Exploring the potential of LLMs, such as GPT models, in healthcare applications — focusing on clinical documentation, decision support, and patient interaction — while addressing bias, privacy, and accountability.
• ML for Personalized Medicine and Precision Healthcare
  Leveraging machine learning to tailor medical treatments to individual patients, considering genetic, environmental, and lifestyle factors, while ensuring fairness, privacy, and ethical use of patient data.
• Ethical ML in Healthcare
  Addressing the ethical implications of deploying machine learning in healthcare, focusing on patient consent, accountability, and minimizing harm.
• Robustness and Security of Healthcare ML Systems
  Ensuring the security, robustness, and resilience of ML models against adversarial attacks, especially in high-risk healthcare applications.
• Responsible Integration of ML into Clinical Decision Support Systems
  Best practices for integrating ML models into clinical workflows to complement healthcare professionals’ expertise and improve patient outcomes.
• Human-in-the-Loop ML Systems for Responsible Healthcare
  Exploring hybrid approaches in which human clinicians work alongside ML models to maintain ethical decision-making and improve model accuracy.
• Legal and Regulatory Aspects of ML in Healthcare
  Examining the legal challenges and regulatory frameworks associated with ML in healthcare, ensuring compliance with laws such as GDPR and HIPAA.
• ML for Healthcare Accessibility and Policy
  Investigating how ML can improve access to healthcare, reduce costs, and influence healthcare policy and resource allocation.
• Safe and Responsible Transfer Learning in Healthcare Applications
  Leveraging pre-trained models and adapting them for use across different healthcare domains, ensuring scalability and generalizability, while addressing ethical concerns such as fairness, privacy, explainability, and safety.
• Benchmarking ML-based Approaches in Responsible Healthcare
  Developing benchmarks — including datasets, metrics, and protocols — for assessing ML-based solutions in responsible healthcare.

Not sure if your work fits the workshop?
If you are interested in submitting but are unsure whether your work aligns with the workshop themes, please feel free to reach out to the organizers. We would be happy to answer any questions and provide guidance!