Image credit: Diego Delso, delso.photo, License CC BY-SA
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This workshop will take place at ECML PKDD on 19 September 2025, in Porto, Portugal. It will be a half-day event.
While Machine Learning (ML) holds considerable promise for enhancing clinical practice and patient outcomes, its deployment necessitates careful consideration of its impact on fairness, privacy, and explainability to ensure that ML-supported solutions serve all stakeholders including communities, patients, clinicians, payers, and policy-makers. Achieving these goals requires active engagement of all interested parties, which is challenging given differences in technical understanding of ML, healthcare goals, patient behavior, and economic policy. This workshop aims to foster an interdisciplinary exchange between ML researchers and healthcare professionals, addressing key methodological issues, domain-specific constraints, and best practices. Through invited talks, paper presentations, and panel discussions, the workshop will contribute to advancing research on the responsible integration of ML in healthcare, promoting solutions that balance technical rigor with real-world applicability. The overarching goal is to promote the development and implementation of more responsible, impactful, and safe machine learning applications in healthcare.
A defining feature of RHCML is its commitment to interdisciplinary collaboration. We strongly encourage submissions and participation from both machine learning researchers and healthcare researchers and professionals. This workshop aims to establish a lasting network of collaborators—clinicians, ML scientists, policy experts, and more—who can jointly tackle challenges such as bias mitigation, interpretability, ethical deployment, and data governance.
We are pleased to acknowledge our partnership with Instats. Instats is a global platform offering live-streamed and on-demand research training across statistics and social, health, and life sciences. Currently their platform contains 28 courses on machine learning, and 1 course specifically dedicated to machine learning and health sciences.
RHCML 2025 is proud to partner with Instats to recognize outstanding contributions through:
- Best Student Contribution Award: 1-year Instats subscription
- Best Researcher Contribution Award: 1-year Instats subscription
All workshop participants will also receive exclusive discount codes for Instats.
Pratik Gajane, Patrick Marcel, Christel Vrain, Thi-Bich-Hanh Dao (University of Orléans) and Kostas Stefanidis (Tampere University)
rhcml-org@googlegroups.com