Days
Hours
Minutes
Seconds
AIBio 2025 explores the transformative role of artificial intelligence (AI) in biomedical research, with a focus on medical imaging, multi-omics, clinical data, and digital health. Biomedical data is inherently complex, characterized by heterogeneity, high dimensionality, and scalability challenges, making it difficult to extract meaningful insights. AI provides powerful tools to address these challenges, driving breakthroughs in disease diagnostics, personalized treatment strategies, and healthcare efficiency.
This workshop emphasizes pathology and omics data, where AI has demonstrated immense potential in disease understanding, molecular profiling, and tissue analysis. However, AIBio 2025 also broadens its scope to include translational medicine, digital health, and the role of telecommunications technologies in biomedical AI. This includes advancements in AI-driven telemedicine, edge and cloud computing for biomedical data analysis, 5G/6G applications in digital health, and secure AI models for biomedical data over networks, enabling real-time diagnostics, remote patient monitoring, and scalable healthcare solutions.
AIBio 2025 also emphasizes the ethical considerations and interpretability of AI models in clinical settings, focusing on strategies to mitigate biases related to gender, ethnicity, and age through fair machine learning techniques. Ensuring that AI systems are transparent, interpretable, and equitable is vital for their integration into healthcare practices.
By bringing together experts from diverse fields, AIBio 2025 aims to bridge the gap between AI research, telecommunications, and real-world medical applications, driving practical and impactful advancements in healthcare.
The AIBio 2025 workshop invites researchers, clinicians, data scientists, and industry professionals to submit their latest findings on AI-driven biomedical research. We seek high-quality, original contributions addressing AI applications in biomedical data, including but not limited to:
Authors must submit original, unpublished research contributions in English, formatted according to the Springer Communications in Computer and Information Science (CCIS) series guidelines. Manuscripts should be prepared using the official Springer LaTeX or Microsoft Word templates, available at Springer’s Author Guidelines.
Authors can submit papers in the following categories:
Authors must submit their manuscripts electronically in PDF format via the official submission system: Chairing Tool AIBio2025.
The review process follows a double-blind policy, meaning that:
Each paper will be subject to a rigorous peer-review process by at least three experts in the field. Papers will be evaluated based on the following criteria:
Authors must ensure that their papers do not contain plagiarized content or overlapping submissions to other venues. Papers that do not comply with the formatting, length, or anonymization requirements will be rejected without review.
The AIBio workshop registration policy follows that of the main ECAI conference. Details can be found https://www.ecai2025.eu/registration.
To attend the AIBio workshop, at least one author of each accepted paper is required to register for the ONLY WEEKEND option by the early registration deadline. Authors also have the option to register for the ECAI main conference + WEEKEND. However, please note that registering solely for the ECAI main conference does not grant access to the AIBio workshop.
At least one author of each accepted paper must register for ONLY WEEKEND or ECAI main conference + WEEKEND by early registration deadline, and present their work at the workshop. The presentation is a mandatory requirement for inclusion in the final proceedings.
The conference Proceedings will be published and indexed by the Communications in Computer and Information Science (Springer CCIS) and indexed in major digital libraries, including: Scopus, EI-Compendex, DBLP, Google Scholar, Additional Information. Please note that for a paper to be published, at least one of its authors must register for ONLY WEEKEND or ECAI main conference + WEEKEND by early registration deadline.
All deadlines are at the end of the day specified, Anywhere on Earth (AoE) (UTC-12).
Dr Craig A. Glastonbury holds a PhD in computational biology from King’s College London (2013–2017), where he focused on mapping tissue-specific eQTLs across multiple human tissues. He then worked as a Postdoctoral Fellow in Cecilia Lindgren’s lab, applying machine learning to histology.
From 2019 to 2022, Craig was a lead ML researcher at BenevolentAI, focusing on human genetics for target discovery and ML-based patient stratification. His broader research combines histopathology imaging, machine learning, and human genetics. At Human Technopole, his group investigates how genetic variation influences quantifiable phenotypes extracted from diverse biomedical imaging modalities.
He serves on the organizing committee of the International Common Disease Alliance (ICDA), is a guest associate editor for machine learning at AHA Circulation, and an honorary ML researcher at the University of Oxford.
Federico Cabitza (BSc, MEng, PhD) is an Associate Professor at the University of Milano-Bicocca, where he leads the Modeling Uncertainty, Decisions, and Interactions Laboratory (MUDILab) and teaches courses in human-computer interaction and decision support.
He has collaborated extensively with hospitals in Milan and co-founded the Medical AI Laboratory. His research focuses on designing and evaluating AI systems for healthcare decision-making and understanding their impact on organizations and workflows.
Author of over 150 publications, Prof. Cabitza has co-chaired international workshops, is listed among Stanford’s Top 2% Scientists, and co-authored the book Artificial Intelligence: The Use of the New Machines with Luciano Floridi.
Despite widespread use, AI systems in healthcare are often evaluated solely by accuracy. In this talk, Prof. Cabitza questions this approach, proposing a multidimensional framework for model evaluation.
Drawing from recent lab developments, he introduces new metrics and visualization tools that reflect data reliability, case similarity, and clinical utility. He will present a public platform that applies these insights, helping stakeholders understand AI behavior beyond averages—especially in uncertain and diverse clinical scenarios.
The following experts form the program committee for AIBio 2025:
For any request you might have, please contact cristian.tommasino@unina.it.