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Held in conjunction with: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2026
Healthcare data now comes from many sources, including medical images, clinical text, EHRs, wearables, and IoT devices. These data sources offer complementary views of patient health, but they are difficult to integrate because they differ in format, scale, timing, and reliability. Multimodal learning offers a promising way to combine these data sources, supported by advances in transformers, contrastive learning, and large-scale pretraining. However, important challenges remain, including missing or asynchronous data, domain variability, clinical interpretability, generalization across institutions, and deployment in real-world healthcare settings.
AMLD-HD 2026 will bring together researchers and practitioners from machine learning, biomedical informatics, medical imaging, and digital health to discuss methods, challenges, and applications for building robust, interpretable, and clinically relevant multimodal AI systems.
RESEARCH TOPICS
We invite high-quality, original submissions to our workshop focused on multimodal healthcare data analytics and intelligent systems for advancing healthcare. The aim is to foster interdisciplinary discussion and collaboration among researchers, practitioners, and industry professionals working at the intersection of healthcare, data science, and IoT technologies.
This workshop will serve as a forum for presenting recent advances, challenges, and future directions in data-centric and multimodal healthcare systems. We welcome novel contributions that address theoretical, methodological, computational, and applied aspects of IoT and data analytics in healthcare.
Topics of interest include, but are not limited to:
Multimodal healthcare data analytics
Learning from Heterogeneous and Temporal Data
Data mining and knowledge discovery for healthcare
IoT-enabled patient monitoring
Clinical decision support
Clinical evaluation and translation
Cloud-based and remote healthcare services
Scalable machine learning frameworks for healthcare
Optimization of healthcare systems
Novel visualization techniques for healthcare data
Wearable sensors for real-time healthcare data analytics
Large language models and generative AI in healthcare
Healthcare data fusion, integration, and knowledge engineering
Applications of multimodal learning in healthcare
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
Presentation Format: Hybrid
AUTHOR GUIDELINES
Please submit a full-length paper (up to 8 page IEEE 2-column format) through the online submission system (you can download the format instruction here: http://www.ieee.org/conferences_events/conferences/publishing/templates.html). The paper submission is double blind. Please don’t include any authors and/or affiliations in the paper.
Electronic submissions (in PDF or Postscript format) are required. Selected participants will be asked to submit their revised papers in a format to be specified at the time of acceptance. For submission, please direct to this link.
All accepted papers will be included in the main conference proceedings which are included in the IEEE digital library indexed by Google Scholar and Scopus.
WORKSHOP SCHEDULE : TBA
INVITED SPEAKERS
Professor Farhana Zulkernine, School of Computing, Queen's University, Canada.
Farhana Zulkernine is a Professor and the Director of the Bigdata Analytics and Management Laboratory (BAM Lab) at the School of Computing, Queen's University. She holds a Ph.D. degree from Queen's University and is a member of Professional Engineers of Ontario. She has more than 15 years of international work experience in three continents in software design, analysis and research and has collaborated with Pfizer, Roche, CA Technologies, IBM, SAP and Fondazione Bruno Kessler in multiple countries across the globe. Her research interests include Artificial Intelligence (AI), service computing, big data analytics and management, and cognitive computing. She has taught a wide number of courses in database management systems, cognitive science and machine learning and organized multiple local and international conferences on AI and Digital Health. Her research has been funded by IBM, CFI, MITACS, NSERC Discovery and CREATE, OCE VIP, CUTRIC, CIMVHR, SOSCIP and Queen's. She has published in many reputed journals and international conferences and served on a variety of conference program and grant committees as an expert in big data and machine learning.
Dr. Haruna Isah, Centre for Applied AI , Sheridan College, Canada.
Dr. Haruna Isah is the Associate Director of Sheridan’s Centre for Applied AI (CAAI), focusing his efforts on driving impactful research and industry partnerships in the area of development and application of artificial intelligence in research. Haruna is an experienced leader in artificial intelligence and cybersecurity with over a decade of expertise in applied research, strategic planning, and fostering innovation. His work bridges academia and industry, delivering transformative AI solutions while creating value for students, empowering faculty, and enhancing community outcomes.
SESSION CO-CHAIRS
Dr. Sazia Mahfuz
Assistant Professor
Jodrey School of Computer Science
Acadia University, Canada
email: sazia.mahfuz@acadiau.ca
Nafiz Sadman
PhD Student
School of Computing
Queen's University, Canada
email: sadman.n@queensu.ca
TECHNICAL PROGRAM COMMITTEE
Hanady Abdulsalam
Kuwait University, Kuwait
Lydia Bouzar-Benlabiod
Acadia University, Canada
Andrew McIntyre
Acadia University, Canada
Hasan Zafari
Quantiphi, Canada
Jing Tao
Queen’s University, Canada
Farida Mohamed
Queen’s University, Canada
Aman Anand
Queen’s University, Canada
Amir Eskandari
Queen’s University, Canada
Ahmed Harby
Providence Care Hospital Ontario, Canada
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