Dallas, USA, December 1-4 2026
Workshop link: BIBM 2026
Organizers: Pierangelo Veltri, University of Calabria
Patrizia Vizza, University of Calabria
Raffaele Giancotti, University of Calabria
Clinical and biological datasets are cornerstones of modern medical advancement and are essential for both advancing disease research and promoting wellness. The implementation of efficient data management and robust processing pipelines is a critical necessity, representing also the key to mitigating pandemic risks, optimizing healthcare delivery, and ensuring the resilience of global health systems. In this context, Artificial Intelligence (AI) and Large Language Models (LLMs) have emerged as transformative forces. They bridge the gap between raw information and clinical application, using advanced natural language processing to synthesize fragmented medical literature and electronic health records into coherent, actionable knowledge. This computational synergy enhances the precision of diagnostic tools and facilitates the development of generative models able of simulating disease progression and therapeutic outcomes.
Comprehensive analysis of clinical and wellness-related data offers an opportunity to extract actionable insights from different sources. These sources range from structured biological laboratory measurements and genomic sequences to unstructured textual descriptions and high-resolution biomedical imaging. Furthermore, the systematic analysis of clinical and hospital information facilitates critical administrative functions, including cost-benefit analysis and large-scale disease surveillance. The integration of molecular data with familial and environmental context allows to identify and extract disease correlations and risk factors. This multi-modal approach aims to shift toward personalized medicine and proactive wellness strategies, enhancing the overall quality of life.
This workshop provides an interdisciplinary forum for researchers and practitioners in computer science, bioengineering, and clinical medicine to present and share their latest research focused on leveraging data-driven methodologies to enhance human health. The workshop represents an opportunity to create a collaboration space for a proactive discussion on the importance of automated processes, driven by advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), in enhancing the quality of life through prevention and sophisticated information extraction methods and systems, as well as enrichment techniques. Moreover, the workshop will explore how these AI-driven techniques can bridge the gap between raw data and actionable medical insights by scaling massive datasets and integrating Generative AI within the life sciences, fostering a new era of precision healthcare.
TOPICS OF INTEREST
The workshop is seeking original research papers presenting applications of algorithms and solutions applied to biomedicine, clinical applications and lifestyle. Topics of interest include, but are not limited to:
1. Data Integration for Personalized Medicine (e.g., Techniques for integrating diverse clinical, biological, and environmental data, identifying disease correlations and risk factors);
2. Advanced Data Management and Processing Pipelines (e.g., robust data processing pipelines, automating data collection, processing, and analysis in healthcare);
3. AI and Machine Learning in Clinical Data Analysis (e.g., applications of artificial intelligence and machine learning, AI for advanced data interpretation in clinical and biomedical contexts, machine learning algorithms for diagnostic and personalized treatments);
4. Biomedical Imaging and Image Processing (e.g., processing and analyzing biomedical images, algorithms for the analysis of clinical and biological data sources, AI-driven approaches for automated image interpretation in clinical practice)
5. Generative AI and Data Augmentation in Healthcare (e.g., exploring generative AI applications for healthcare data generation; data enhancing richness and diversity through synthetic data in clinical studies)
6. AI-Driven Preventive Healthcare and Wellness (e.g., AI technologies in preventive healthcare initiatives, approaches to wellness improvement through data-driven insights).
7. Real-Time Data Analytics for Healthcare Decision Making (e.g., data analysis to support clinical decision-making, integrating wearable devices and sensors for patient monitoring)
8. Emerging Technologies in Bioengineering for Wellness (e.g., Innovative bioengineering techniques for improving health outcomes, bioengineering in developing wearable health devices and diagnostic tools)
Important Dates:
Sept 27, 2026: Due date for full workshop papers submission
Oct 18, 2026: Notification of paper acceptance to authors
Nov 8, 2026: Camera-ready of accepted papers
Dec 1-4, 2026: Workshops
Paper Submission:
Length of paper
Full-paper: 8 pages
Short-paper: 2 pages
Please submit a paper (up to 8 page IEEE 2-column format) through the online submission system (you can download the format instructions here (http://www.ieee.org/conferences_events/conferences/publishing/templates.html).
All accepted accepted will be included in the main conference proceedings which are included in the IEEE digital library indexed by Google Scholar and Scopus.
Special Issues on important Journals are also planned.
Online Submission Web Site:
Program Chairs
Pierangelo Veltri - University of Calabria, pierangelo.veltri@dimes.unical.it
Patrizia Vizza – University of Calabria, patrizia.vizza@dimes.unical.it
Raffaele Giancotti – University of Calabria, raffaele.giancotti@dimes.unical.it
Pietro Hiram Guzzi (University of Catanzaro, Italy)
Giuseppe Pozzi (Politecnic University of Milano, IT)
Pierre Baldi (University of Irvine California)
Elena Succurro (University of Catanzaro, Italy)
Giuseppe Pozzi (Politenico di Milano, IT)
Giuseppe Tradigo (University ecampus, Novedrate, Italy)
Ester Zumpano (University of Calabria, Italy)
Mattia Cannistrà (University of Catanzaro, Italy)