A special session on trustworthy, sustainable, and governance-aware computational intelligence for biomedicine.
This special session brings together researchers working at the intersection of computational intelligence, biomedical engineering, bioinformatics, and digital health to discuss intelligent systems that are not only high-performing, but also interpretable, privacy-conscious, regulation-aware, and sustainable by design.
Advanced computational intelligence methods for high-impact biomedical and health-related problems.
Interpretability, fairness, privacy, and compliance integrated into system development.
Resource-aware and operationally sustainable approaches for biomedical AI deployment.
Governance-aware computational intelligence architectures for biomedical systems
Federated, privacy-preserving, and distributed learning in healthcare ecosystems
Energy-efficient and sustainable intelligent methods for omics and medical imaging
Interpretable, accountable, and regulation-conscious digital health systems
As intelligent systems become embedded within real biomedical and clinical infrastructures, their scientific value depends not only on predictive performance, but also on transparency, accountability, privacy, sustainability, and readiness for deployment in regulated settings.
Responsible and Sustainable Computational Intelligence for Biomedicine
Computational intelligence methods are increasingly integrated into biomedical research, digital health platforms, and bioinformatics pipelines. However, beyond predictive performance, modern biomedical AI systems must address broader challenges including interpretability, fairness, privacy protection, regulatory compliance, and computational sustainability.
This special session aims to create a forum for research that combines advanced computational intelligence with responsible system design. The focus is on approaches that support trustworthy, explainable, energy-efficient, and governance-aware AI systems capable of operating in real biomedical and clinical environments.
Novel computational intelligence approaches addressing complex biomedical datasets such as medical imaging, omics data, and clinical health records.
Methods that integrate transparency, explainability, privacy preservation, fairness, and governance considerations into AI system design.
Intelligent infrastructures supporting digital health platforms, distributed biomedical data ecosystems, and next-generation clinical decision systems.
Energy-efficient and resource-aware AI techniques enabling scalable biomedical intelligence with reduced environmental footprint.
Areas of contribution
We invite original research contributions in areas including, but not limited to:
Energy-efficient computational intelligence for medical imaging and omics data
Federated and privacy-preserving learning in healthcare
Explainable and interpretable AI in bioinformatics and computational biology
Generative models for biomedical applications
Multi-agent systems in digital health environments
Data governance and regulation-aware AI architectures
Sustainable computing for large-scale biological modeling
Computational intelligence for climate-health and environmental monitoring
Fairness and robustness in biomedical AI
Lifecycle assessment of intelligent systems in healthcare
Distributed ledger technologies for secure digital health infrastructures
Who should submit
Researchers in computational intelligence, bioinformatics, biomedical engineering, and healthcare informatics
Digital health and clinical decision-support system designers
Federated and privacy-preserving learning researchers in healthcare ecosystems
Experts in trustworthy, interpretable, and sustainable intelligent systems
Researchers working on governance, regulatory compliance, and ethical AI in high-risk biomedical domains
Distributed systems and secure data infrastructure researchers supporting health and bio-ecosystems
Industry practitioners in digital health platforms, biomedical analytics, and regulated AI deployment
An interdisciplinary organizing team
Dr. Polat Goktas is an Assistant Professor in the Faculty of Engineering and Natural Sciences at Sabancı University, Türkiye. His research spans artificial intelligence, machine learning, data mining, and explainable AI, with a focus on sustainable intelligent systems, privacy-aware learning, and digital healthcare applications. He previously served as a Senior Researcher at University College Dublin and CeADAR, and was a Fulbright Doctoral Research Fellow at Harvard Medical School and Massachusetts General Hospital.
Dr. Elif Calik is a Digi+ MSCA COFUND Postdoctoral Fellow and researcher at the ADAPT Research Centre and the School of Computer Science, University of Galway. Her work focuses on blockchain technology, smart contracts, medical informatics, privacy, trustworthiness-by-design, ethics, and multi-stakeholder data governance in healthcare ecosystems.
Dr. Malika Bendechache is a Lecturer Above the Bar in the School of Computer Science at the University of Galway and an investigator at ADAPT and Lero Research Centres. Her research covers big data analytics, machine learning, data governance, privacy, and trustworthy intelligent systems, with applications in healthcare and complex data-driven environments.