The Official Journal of the European Alliance of Medical and Biological Engineering and Science (EAMBES).

Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.

Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.

Special Issue on Advancing Explainable, Interpretable, and Ethical Artificial Intelligence in Healthcare
Submission Date: 2026-05-31

Artificial Intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities for diagnosis, treatment, drug discovery, personalized medicine, and improved patient outcomes. However, the increasing complexity of AI models, particularly deep learning, often leads to "black box" systems, raising concerns about transparency, trust, and accountability in critical healthcare applications. This special issue aims to explore the cutting-edge advancements and address the critical challenges associated with the deployment of AI in healthcare, with a particular focus on Explainable AI (XAI), Interpretable Machine Learning (IML), and the overarching Ethical Aspects. We invite researchers, academics, innovators, practitioners, and enthusiasts to contribute original research that sheds light on how to build and deploy AI systems in healthcare that are not only accurate but also transparent, understandable, trustworthy, and ethically sound.

Guest editors:

Dr. Armando Ruggeri
University of Messina
armruggeri@unime.it

Prof. Dr. Mario Antonio Ribeiro Dantas
Federal University of Juiz de Fora
mario.dantas@ufjf.br

Dr. Fabrizio Celesti
University of Siena
fabrizio.celesti@unisi.it

Prof. Dr. Yu-Sheng Lin
Southern Taiwan University of Science and Technology
yushenglin@mail.ncku.edu.tw

Special issue information:

This special issue welcomes submissions that address, but are not limited to, the following topics:

    Development and application of novel XAI/IML techniques for various healthcare domains (e.g., medical imaging, genomics, tele-health, electronic health records, clinical decision support).
    User-centered design of XAI/IML interfaces for clinicians, patients, and other stakeholders.
    Explainability/Interpretability/Ethics for risk stratification, disease prediction, and treatment response prediction.
    Integration of domain knowledge with XAI/IML techniques in healthcare.
    Explainability and interpretability of generative AI models in healthcare contexts. 
    Scalable performant machine learning applications in healthcare.
    Ethical considerations and challenges related to the use of synthetic data and generative models in healthcare.
    The impact of AI on the patient-physician relationship and trust.
    Ethical considerations related to the use of AI for diagnosis, prognosis, and treatment decisions.

Manuscript submission information:
Final Manuscript Submission Deadline: 31/05/2026
 
For any inquiries about the appropriateness of contribution topics, please contact Dr. Armando Ruggeri via armruggeri@unime.it.

Please refer to Guide for authors - Biomedical Signal Processing and Control - ISSN 1746-8094 | ScienceDirect.com by Elsevier to prepare your manuscript, and select the article type of “VSI: Advancing Ethical and XAI in Healthcare” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. 

Both the Guide for Authors and the submission portal could also be found on Biomedical Signal Processing and Control | Journal | ScienceDirect.com by Elsevier.

Keywords:

Explainable AI; Interpretable Machine Learning; Trustworthy; Ethical; Healthcare