Vice-Dean of Internationalization & Global Health and a Professor at the University of Ottawa’s Faculty of Medicine. He is a Maternal–Fetal Medicine specialist and Senior Scientist whose research focuses on perinatal epidemiology, population health, and large-scale clinical datasets. His work increasingly integrates artificial intelligence to improve outcomes in maternal and newborn health.
Professor in the School of Epidemiology & Public Health at the University of Ottawa and Tier 1 Canada Research Chair in Medical AI. He leads the Electronic Health Information Laboratory and focuses on privacy-enhancing technologies, de-identification, synthetic data generation and secure health-data sharing for AI applications.
Clinician-scientist affiliated with the University of Ottawa and CHEO Research Institute. Her work centers on health data governance, de-identification, and privacy-preserving methods for clinical research.
Affiliated with the University of Ottawa’s Faculty of Medicine. Cognitive scientist with over 20 years of experience in human-centred design and AI innovation. His work bridges psychology, user experience, and technology to improve how people interact with intelligent systems.
Scientists interested in applying AI to healthcare research, including areas such as diagnostics, predictive modeling, and precision medicine.
Physicians and healthcare professionals interested in understanding and integrating AI tools into clinical practice for improved decision-making, patient care, and disease management.
DDI Auditorium
Up to auditorium capacity
Explain and differentiate the foundational concepts of artificial intelligence, machine learning, deep learning, and large language models, and analyze their current and emerging applications in healthcare delivery and biomedical research.
Apply AI tools such as large language models and predictive analytics to real-world healthcare and research tasks, including literature reviews, protocol design, diagnostics, and precision medicine scenarios.
Critically evaluate AI models and applications by examining dataset requirements, validation approaches, generalizability, bias, and ethical considerations, and justify decisions about adoption in clinical and research settings.
Demonstrate practical skills in prompt engineering, hands-on use of AI platforms, and model interpretation, while assessing the implications of governance, data privacy, and regional regulations for responsible AI use in healthcare.
Morning Session
9:00 - 10:00
Dr. Mark Walker
Artificial Intelligence in Healthcare: Foundations & Opportunities
Overview of AI, ML, DL, and their applications in health research and clinical practice
Impact on health care and Health Research as co-intelligence
10:00-10:45
Dr. Lisa Pilgram
Large Language Models: How They Work and Their Relevance to Medical Research and practice
Basics of LLMs.
Examples in use in clinical care. Use case in literature searching and evidence.
11:00-12:00
Dr. Khaled El Emam
LLMs in Practice: Use Cases in Biomedical Research
You best peer for brainstorming.
Automating literature reviews.
PowerPoint presentations in minutes
Break
Debate on AI
Afternoon session
1:00-2:00
Dr. John Whalen
Prompt Engineering for Healthcare and Research
How to design powerful prompts.
Strategies to reduce bias and improve accuracy.
Designing research protocols.
Creating web sites with no coding needed
2:00-3:30
Dr. Khaled El Emam
Hands-On Lab: LLM Applications in Research
Participants work on:
Creating a structured literature search.
Drafting an ethics submission summary.
3:30-4:00
Wrap up
Morning Session
9:00-10:00
Dr. Mark Walker
Deep Learning in Imaging and Diagnostics
AI in radiology, pathology, and ophthalmology.
Diabetes-related examples (retinopathy, nephropathy)
10:00-10:45
Dr. Lisa Pilgram
Predictive Modeling & Precision Medicine
AI for risk prediction (e.g., HbA1c trends, complications, hospital readmissions).
11:00-12:00
Dr. Mark Walker
Critical Appraisal of AI Models
Dataset requirements, validation methods, generalizability, bias detection.
Frameworks for deciding whether to adopt AI in clinical/research settings.
Break
Afternoon session
1:00-2:00
Dr. Khaled El Emam
Ethics, Governance, and Data Privacy in AI for Healthcare
International best practices.
Regional considerations (Kuwait/Middle East context).
Data sharingand de-identification.
2:00-3:30
Dr. Lisa Pilgram
Hands-On Lab: Building & Interpreting a Predictive Model
Using anonymized diabetes dataset (e.g., lab + clinical data).
No need to code. Just prompt.
Use explainability tools to interpret results.
3:30-4:00
Wrap up
© 2025 Dasman Diabetes Institute. All rights reserved.