eXplainable AI in Healthcare 2023 (XAI-Healthcare)

Table of Contents

The purpose of XAI-Healthcare 2023 event is to provide a place for intensive discussion on all aspects of eXplainable Artificial Intelligence (XAI) in the medical and healthcare field. This should result in cross-fertilization among research on Machine Learning, Decision Support Systems, Natural Language, Human-Computer Interaction, and Healthcare sciences. This meeting will also provide attendees with an opportunity to learn more on the progress of XAI in healthcare and to share their own perspectives. The panel discussion will provide participants with the insights on current developments and challenges from the researchers working in this fast-developing field.

Explainable AI (XAI) aims to address the problem of understanding how decisions are made by AI systems by designing formal methods and frameworks for easing their interpretation. The impact of AI in clinical settings and the trust placed in such systems by clinicians have been a growing concern related to the risk of introducing AI into the healthcare environment. Explainable AI in healthcare is a multidisciplinary area addressing this challenge by combining AI technologies, cognitive modelling, healthcare science, ethical and legal issues. Therefore, this workshop aims to outline future directions for XAI and highlight the importance of developing algorithmic solutions that can enable XAI driven decision making in high-stakes healthcare problems.

Updates and Previous Editions: https://www.um.es/medailab/events/XAI-Healthcare/


Concha Bielza, Dept. of Artificial Intelligence, Universidad Politecnica de Madrid.

Pedro Larrañaga, Dept. of Artificial Intelligence, Universidad Politecnica de Madrid.

Primoz Kocbek, Faculty of Health Sciences, University of Maribor.

Jose M. Juarez, Faculty of Computer Science, University of Murcia.

Gregor Stiglic, MedAI Lab, Faculty of Health Sciences, University of Maribor.

Alfredo Vellido, Universitat Politecnica de Catalunya and UPC BarcelonaTech.

Important dates

Topics of interest

We expect the contributions received to describe explanation methods, AI techniques and a targeted healthcare problem. Some examples are provided below for guidance, but the list of topics is not limited to these specific methods, techniques and problems.

Explanation Approaches

● Model agnostic methods
● Feature analysis
● Visualisation approaches
● Example and counterfactuals based explanations
● Fairness, accountability and trust
● Evaluating XAI
● Fairness and bias auditing
● Human-AI interaction
● Human-Computer Interaction (HCI) for XAI
● Natural Language Processing (NLP) Explainability

AI Techniques

● Blackbox ML approaches: DL, random forest.
● Interpretable ML models: Rules, Trees, Bayesian networks, etc.
● Statistical models and reasoning
● Case-based reasoning
● Natural language processing and generation
● Abductive Reasoning

Target healthcare problems

● Infection challenges (COVID, Antibiotic Resistance, etc.)
● Trustworthy AI
● Chronic diseases
● Ageing & home care
● Diagnostic system

Invited Speaker

Prof. Mihaela van der Schaar

Professor of Machine Learning, Artificial Intelligence and Medicine at  the University of Cambridge. Fellow at The Alan Turing Institute in London. ELLIS Fellow.

Title of the talk: TBA

About M. van der Schaar and her lab.

About her on Wikipedia.


This workshop will be organised as a face-to-face event. XAI-Healthcare will include paper presentations and a keynote talk related to the workshop topics listed above. All submitted papers will be subject to a review by the workshop Program Committee. Based on the number of highquality submissions we will define the length of the presentations that will be followed by time for questions and discussion from the audience. We plan to have a panel discussion together with the organisers and the attendees.


We are aiming at providing arXiv open-access proceedings, gathering all papers presented at the workshop. Furthermore, we are also considering a proposal of a special issue of a journal like Journal of Healthcare Informatics Research (JHIR) or a similar specialised journal.

Program Committee

Alejandro Rodriguez, Universidad Politecnica de Madrid, Spain

Bernardo Canovas-Segura, University of Murcia, Spain

Buzhou Tang, Harbin Institute of Technology, China

Carlo Combi, University of Verona, Italy

Caroline Konig, Universitat Politecnica de Catalunya, Spain

Daniele Magazzeni, King’s College London, UK 

Giorgio Leonardi, Piemonte Orientale University, Italy

Huang Zhengxing, Faculty of Biomedical Engineering,China

Jean-Baptiste Lamy, LIMICS, France

Jose M. Alonso, Universidad de Santiago de Compostela, Spain

Lluis Belanche, Universitat Politecnica de Catalunya, Spain

Milos Hauskrecht, University of Pittsburgh, USA

Nava Tintarev, Maastricht University, The Netherlands

Niels Peek, University of Manchester, United Kingdom

Paulo Felix, Universidad de Santiago de Compostela, Spain

Pedro Cabalar, University of Coruna Ping Zhang, Ohio State University, USA

Przemyslaw Biecek, Warsaw University of Technology, Poland

Simone Stumpf, City University London, United Kingdom

Zhe He, Florida State University, USA

Paper submission

Papers should be submitted to the XAI-Healthcare Easy Chair Website at https://easychair.org/my/conference?conf=xaihealthcare2023.

Papers should be formatted according to Springer Lecture Notes Format, either for LaTeX or for Word. Springer’s LaTeX templates are available in Overleaf (overleaf template). The workshop features regular papers in two categories: short papers (up to 5 pages) describing work-in-progress and full papers (up to 10 pages) describing original and solid results.