Artificial Intelligence in Healthcare (Intensive 14 Weeks)
Course Description
This intensive course explores foundational to advanced topics in applying Artificial Intelligence (AI) to health care. Over 14 weeks, students will gain both conceptual knowledge and hands-on experience in designing, implementing, evaluating, and deploying AI systems in clinical and operational healthcare settings. Key dimensions include medical data types, predictive modeling, imaging, natural language processing, ethics, regulation, and integration into clinical workflows. By the end, students will have a practical project that demonstrates capability to translate AI techniques into real healthcare impact.
By the end of the course, students should be able to:
- Understand core AI / ML concepts, techniques, and architectures relevant to health care.
- Work with health-data: acquisition, cleaning, privacy, structuring, and multimodal data.
- Apply AI to real clinical problems (diagnosis, prognosis, treatment optimization, medical imaging, etc.).
- Understand and critique ethical, legal, regulatory, interpretability, fairness, equity issues in healthcare AI.
- Design AI systems / pipelines in healthcare settings, including evaluation, validation, deployment, and monitoring.
- Communicate effectively with technical & non-technical stakeholders about AI in health care.
Committed to Your Success Every Step of the Way!
Week 1
Introduction: AI & Healthcare Landscape
What AI is, its history; current & emerging use cases; stakeholders in healthcare; overview of health system challenges.
Week 2
Health Data Fundamentals
Types of health data (EHR, imaging, time series, genomics, sensor), data quality, privacy & security (HIPAA, GDPR etc.), data governance.
Week 3
Machine Learning Foundations
Supervised / unsupervised learning; regression, classification; evaluation metrics; overfitting & validation.
Week 4
Deep Learning, Neural Networks, & Multimodal Data
CNNs, RNNs/LSTMs/transformers; combining modalities (image + text + signal); basics of architectures used in diagnosis, imaging, etc.
Week 5
Medical Imaging & Computer Vision Applications
Detection, segmentation; tools & frameworks; case studies (radiology, pathology); challenges (annotation, bias, interpretability).
Week 6
Natural Language Processing (NLP) in Healthcare
Clinical notes, information extraction, summarization, language models; limitations and languages (privacy, unstructured data).
Week 7
Predictive Analytics, Risk Stratification & Treatment Optimization
Time‐series forecasting; survival analysis; cohort stratification; personalized treatment plans.
Week 8
Drug Discovery / Genomics / Bioinformatics Applications
Use of AI in genomics, biomarkers, variant calling; accelerating drug discovery pipelines.
Week 9
Generative AI & Emerging Models
Generative models (e.g. GANs, diffusion models), large language models, data augmentation, synthetic data, generation for imaging/text.
Week 10
Interpretability, Fairness, Bias, Ethics & Liability
Algorithmic transparency; explainable AI; bias in data & predictions; legal / liability issues; ethical frameworks.
Week 11
Regulatory & Policy Issues
Regulatory agencies, standards (FDA, Health Canada, etc.), data privacy laws, clinical trials for AI, post-market surveillance.
Week 12
Deployment, Operationalization & Integration into Clinical Workflows
Infrastructure, scalability, user interfaces, quality control, monitoring, human in the loop, change management.
Week 13
Case Studies / Project Presentations
Review successful & failed implementations. Student / group projects—designing AI solution, or prototype.
Week 14
Future Trends & Course Synthesis
Emerging technologies (edge AI, wearable sensors, AR/VR, telehealth), globalization, AI for low-resource settings. Reflection & how to keep learning; evaluation.
1. Healthcare Data & AI Roles
- Clinical Data Analyst / Healthcare Data Scientist – analyzing patient data, building predictive models.
- Machine Learning Engineer (Healthcare Focus) – designing, training, deploying AI models for imaging, EHR, or clinical decision support.
- AI Research Assistant (Medical AI) – supporting academic or industry research labs in AI for radiology, genomics, etc.
2. Clinical & Administrative Integration
- Clinical Informatics Specialist – bridging AI applications with hospital information systems.
- Digital Health Consultant – advising hospitals, startups, or government on AI adoption, integration, and policy.
- Healthcare Operations Analyst – using AI tools for workflow optimization, resource management, patient flow.
3. Specialized Application Areas
- Medical Imaging AI Specialist – working in radiology/pathology AI startups or hospital imaging labs.
- Genomics / Bioinformatics Analyst – applying AI to precision medicine, genomics, and drug discovery.
- NLP in Healthcare Specialist – extracting and structuring insights from clinical notes, electronic health records.
4. Governance, Policy & Ethics
- AI in Healthcare Policy Advisor / Analyst – working with regulators, governments, or NGOs on AI adoption and safety.
- Ethics & Compliance Officer (AI & Health) – focusing on bias, transparency, and regulatory adherence of AI systems.
5. Entrepreneurship & Industry
- Startup Founder / Product Manager in Digital Health AI – building AI-powered health apps, telemedicine platforms, or decision support tools.
- Health Tech Business Development / Innovation Specialist – working with companies launching AI products for hospitals or clinics.
Salary Ranges (Canada)
Entry-level / Junior Healthcare Data Scientist / ML Engineer (0–2 yrs)
CA$80,000 – CA$110,000
Mid-career (3–5 yrs experience)
CA$110,000 – CA$150,000
Senior / Specialist / Lead ML / Data Scientist roles
CA$150,000 – CA$220,000+
Government / Public Sector
CA$75,000 – CA$100,000 (for many data scientist / health-data roles)
Specific Jobs for Graduates
Healthcare Data & AI Roles
- Healthcare Data Analyst – analyze hospital/clinical data to improve patient outcomes, efficiency, or resource use.
- Machine Learning Engineer (Healthcare Focus) – build and train AI models for diagnosis, imaging, genomics, or drug discovery.
- Clinical Data Scientist – apply AI to electronic health records (EHR), lab data, or clinical trial datasets.
- Bioinformatics Specialist – use AI to analyze genomic, proteomic, or biomedical datasets.
AI in Medical Imaging & Diagnostics
- Medical Imaging AI Specialist – develop and validate AI models for X-rays, MRIs, CT scans, pathology slides.
- Computer Vision Engineer (Healthcare) – apply vision AI to detect anomalies in scans or automate radiology workflows.
- Diagnostic Algorithm Developer – create AI-driven tools that assist doctors in diagnosis and decision support.
Healthcare Technology & Operations
- Health Informatics Specialist (AI/ML) – integrate AI into health information systems, EHR platforms, and clinical workflows.
- AI Product Manager (Health Tech) – oversee development of AI healthcare tools in startups or medtech firms.
- Digital Health Specialist – implement AI in telemedicine, wearable monitoring, and personalized medicine.
- Clinical Decision Support Engineer – design AI tools that recommend treatments or predict patient deterioration.
Research & Policy Careers
- AI Research Associate (Healthcare) – support academic, hospital, or pharma AI research projects.
- Clinical Trial Data Scientist – use AI to optimize recruitment, analyze trial data, and predict outcomes.
- Public Health Data Scientist – apply AI to large population health datasets, epidemiology, or outbreak prediction.
- AI Ethics & Policy Analyst (Health) – work on privacy, bias, and responsible use of AI in healthcare
Employers
- Hospitals & Health Networks: Mayo Clinic, Toronto General Hospital, Alberta Health Services.
- Research Institutes: Canadian Institute for Health Information (CIHI), Vector Institute (Toronto), university labs.
- Health Tech Startups: Deep Genomics, Dialogue Health, Maple, Ada Health.
- Pharmaceuticals & Biotech: Roche, Pfizer, Novartis, Amgen.
- Big Tech in Health AI: Google Health, Microsoft Health AI, IBM Watson Health, Amazon Web Services.