Special Session 2:
Explainable Artificial Intelligence
in Bioengineering (EAIB)
Artificial intelligence is widely adopted in bioinformatics and bioengineering. As a matter of fact when the diagnosis or selection of therapy is no longer performed exclusively by the physician, but to a significant extent by artificial intelligence, decisions easily become nontransparent. The most common application of machine learning algorithms in the bioinformatics and bioengineering context is automatic clinical decision-making. For these tasks, these are several well-known algorithms (artificial neural networks, classifiers, etc.), which are tuned based on (labeled) samples to optimize the classification of unseen instances. A deep understanding of the mathematical details of the decision behind an Artificial intelligence algorithm may be possible for statistics or computer science domain experts. Clearly, when it comes to the fate of human beings, this "developer’s explanation" is not sufficient. The shift from therapy-relevant decisions based on human knowledge to black-box-like computer algorithms makes the decision-making increasingly incomprehensible to medical staff and patients. This has been recognized in the issuance of guidelines, e.g., by the European Union or DARPA (USA), which emphasize the need for computer-based decisions to be transparent and in a form that can be communicated in an understandable way to medical personnel and patients. To address this problem, the concept of explainable artificial intelligence (XAI) is attracting scientific interest. XAI uses a representation of human knowledge, usually (a subset of) predicate logic, for its reasoning, deduction, and classification (diagnosis). The aim of this special session is to boost the research and industrial community in the proposal and development of methodologies aimed to (clearly) explain the clinical decisional process to non-domain experts.
Scope and topics:
Topics of interest include, but are not limited to:
- Explainable artificial intelligence
- Biomedical data mining
- Formal methods in medicine
- Model Checking in clinical contexts
- Interpretable data mining
- Biomedical knowledge representation
- Biomedical knowledge discovery
Chairs:
- Chair Emails
Chair Biographies
Francesco Mercaldo, Researcher of the
University of Molise, Italy.
Francesco Mercaldo:francesco.mercaldo@unimol.it
Antonella Santone: antonella.santone@unimol.it
Pan Huang: panhuang@cqu.edu.cn
Antonella Santone, Associate Professor of the University of Molise, Italy.
Pan Huang, Chongqing University-Nanyang Technological University, China-Singapore.
Technical Committee
- Marcello Di Giammarco, IIT-CNR, Pisa, Italy
- Francesco Mercaldo, Researcher of the University of Molise, Italy Giovanni
- Ciaramella, Scuola IMT Alti Studi Lucca and IIT-CNR, Italy
- Luca Petrillo, Scuola IMT Alti Studi Lucca and IIT-CNR
- Simona Correra, University of Molise, Italy
- Valeria Sorgente, University of Molise, Italy
- Lucia Lombardi, University of Molise, Italy
Paper Submission Instructions
All papers will be double-blind reviewed and must present original work.
- CMT Submission Site
- Select the track: Special Session 2: Explainable Artificial Intelligence in Bioengineering (EAIB)
Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:
Keydates
- Submission due date: August 20, 2025
- Notification of Acceptance: September 10, 2025
- Camera Ready Papers: September 20, 2025
- Pre-registration: September 20, 2025
- Conference: December 3-5, 2025
Registration
In order for your paper to be presented and published in the proceedings, you must register to the conference.
Paper Presentation Instructions
The papers submitted to this track will be presented in person as part of the conference. There is no virtual presentation for this session.
Note: More details about this special session can be explored from here.
ICMLA'25