Explainable and Trustworthy AI for Clinical Decision Support Systems

Topic and Content

Clinical decision support systems are increasingly powered by machine learning and artificial intelligence, but adoption in real-world settings depends on transparency, trust, accountability, and explanations that clinicians can understand and use.

This workshop brings together researchers and practitioners working at the intersection of explainable AI and healthcare. We will focus on interpretable methods for clinical decision support, real-world deployment, validation, and the human and regulatory dimensions of trustworthy healthcare AI.

We will cover a range of topics including but not limited to:

1. Explainable AI methods for clinical decision support systems. Techniques for transparent prediction and explanation in healthcare workflows.
2. Interpretable machine learning for healthcare applications. Approaches that balance predictive performance with clinical usability.
3. Model-agnostic and model-specific explanation techniques. Examples include LIME, SHAP, attention-based methods, and related tools.
4. Explainability for medical imaging, EHRs, and multimodal data. Methods that connect imaging, tabular, and longitudinal healthcare data.
5. Evaluation and validation of explanations in clinical settings. How to measure explanation quality, faithfulness, and clinical usefulness.
6. Bias, fairness, ethics, and regulation. Responsible AI practices for trustworthy deployment in clinical environments.

Paper Submission

Please submit your papers via the workshop submission portal using the button below.

Key Dates

• Sep 27, 2026Full workshop papers due
• Oct 18, 2026Notification of paper acceptance to authors
• Nov 8, 2026Camera-ready version of accepted papers
• Dec 1-4, 2026Workshops during IEEE BIBM 2026, Hybrid Format: both in-person and virtual particiaption will be allowed.

Paper Guidelines

• Use the IEEE style files for conference proceedings, and follow the IEEE BIBM submission instructions and conference publication policy.
• Only original, unpublished work will be considered.
• Accepted papers must be revised and submitted in camera-ready form by Nov 8, 2026.
• Accepted papers must be registered and presented at the workshop to be included in the main Conference Proceedings and for publication in IEEE Xplore.

Organizers

Dr. Rahul Dubey, Assistant Professor Computer Science, Missouri State University, USA.

Email: RahulDubey@MissouriState.edu.

Dr. Tayo Obafemi-Ajayi, Guy Mace Professor of Engineering, Missouri State University, USA.

Email: tayoobafemiajayi@missouristate.edu.

Technical Committee

• Dr. Daniel Hier - Missouri University of Science and Technology, USA
• Dr. Raj Mani Shukla - Anglia Ruskin University, UK
• Dr. Keeley Crockett - Manchester Metropolitan University, UK
• Dr. Hava Siegelmann - University of Massachusetts, USA
• Dr. Marco S. Nobile - Ca’ Foscari University of Venice, Italy

Venue

IEEE BIBM 2026 will be held in Dallas, Texas, USA. The workshop will run as part of the conference workshop program during Dec 1-4, 2026. Participants can join either in-person or virtually.

Q&A

For questions about submissions, the program, or the workshop website, please contact the organizers above.