Explainable Artificial Intelligence in Healthcare Applications

Call for Workshop Papers

With the start of the 21st century, Machine Learning (ML): a sub-field of Artificial Intelligence (AI) has found a good way to move over the big and complex amount of existing data. Thanks to developments in hardware technologies (such as GPU), advances in ML provided ways to deal with real-world problems including the ever-increasing amount of instantaneous type of data. A new era for AI, called Deep Learning (DL) has emerged. However, we are still facing a major issue of having black-box models that do not provide an explanation for the reached decisions (output values) to the extent that humans could trust the feedback given by such intelligent systems. This causes a lack of knowledge about how such a ML-oriented system reached its decisions, what are its limitations, what to do when its decisions are wrong, how to understand solution correctness of the reached decision etc. By moving from that issue, the concept of Explainable Artificial Intelligence (XAI) has been introduced to make intelligent systems more interpretative. In detail, especially rule-based approaches and transparent techniques such as Decision Trees, Bayesian models, and Regression models are widely used to form hybrid models making ML models to have layers of explanation. Since XAI has different topics to research, this workshop aims to gather cutting-edge, latest developments in healthcare applications, and to encourage an open and wide discussion environment for future research. The medical field has great importance in our life and healthcare is a remarkable aspect in which foundations of AI are widely applied. It is thought that XAI ensures a great triggering effect on healthcare. Therefore, submissions including recent developments and both theoretical and applied views are welcome to this workshop.


  • Simulatable XAI models for healthcare applications,
  • Decomposable XAI models for healthcare applications,
  • Algorithmic transparency in XAI for healthcare,
  • XAI models for COVID-19 and pandemics diagnosis,
  • XAI models for cancer diagnosis,
  • XAI models for rare disease diagnosis,
  • XAI models for drug discovery,
  • XAI models for gene-oriented research,
  • XAI models for Internet of Health Things (IoHT),
  • Performance optimization of XAI models for healthcare,
  • Evaluations of transparent techniques for XAI interfaces,
  • User experiences regarding XAI for healthcare applications,
  • Different explanation ways for XAI based healthcare applications,
  • Future of XAI for healthcare applications.

Submit Workshop Paper


All registered papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library.

Proceedings will be submitted for inclusion in leading indexing services, Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL).

Authors of selected best accepted and presented papers will be invited to submit an extended version to Special Issues:

Additionally, selected papers will be considered to be included in:

Paper submission

Papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the Springer format (see Author’s kit section).

Important Dates

Submission Deadline: 15 October 2020
Notification Deadline: 29 October 2020
Camera-ready Deadline: 12 November 2020
Video Submission Deadline: 12 November 2020
Conference: 26 – 27 November 2020

Workshop Chair

Dr. Utku Kose
Suleyman Demirel University, Turkey
[email protected]
[email protected]