AI in Medicine: Foundations and Applications

To equip learners with a clear conceptual and practical understanding of Artificial Intelligence in medicine, covering foundational machine learning, clinical decision support systems, biomedical data analysis, and responsible AI deployment in healthcare.

📅 Day 1: Foundations of AI in Medicine

Session 1: Introduction to AI in Healthcare

• Key concepts of modern Machine Learning in medicine
• Supervised Learning and Self-Supervised Learning
• Role of AI in clinical and research environments

Session 2: Generative AI and Medical AI Pipelines

• Generative modeling in medicine
• Overview of end-to-end AI pipelines
• Integration of AI into clinical and research workflows

🛠️ Hands-on 1

• Building a simple supervised learning model using sample clinical data (guided Google Colab walkthrough)

🛠️ Hands-on 2

• Exploring a pre-trained medical AI model for classification on sample healthcare datasets

🔹 Wrap-up & Q&A

• Key takeaways and preview of Day 2

📅 Day 2: AI Applications in Clinical Practice & Research

Session 1: AI-Assisted Diagnosis and Clinical Decision Support

• AI in clinical workflows
• Case studies in medical imaging, diagnostics, and signal interpretation

Session 2: AI in Biomedical Research

• Data challenges in healthcare AI
• Introduction to medical image and signal interpretation using deep learning

🛠️ Hands-on 1

• Medical image interpretation using a deep learning model on sample diagnostic images

🛠️ Hands-on 2

• Biomedical signal analysis using a guided machine learning notebook

🔹 Wrap-up & Q&A

• Key takeaways and preview of Day 3

📅 Day 3: Evaluation, Regulation & Future of AI in Medicine

Session 1: AI Model Evaluation and Clinical Safety

• Pre-market evaluation of AI systems
• Post-market surveillance strategies
• Model performance, safety, and reliability

Session 2: Ethics, Regulation & Future Directions

• Regulatory frameworks for AI in medicine
• Ethical AI and responsible deployment
• Future of generative AI in clinical practice and research

🛠️ Hands-on 1

• Evaluating AI model performance and bias using sample datasets and metrics

🛠️ Hands-on 2

• Designing a mini regulatory and governance checklist for a medical AI system

🔹 Closing Session

• Career guidance and future opportunities
• Certificate information
• Resource library walkthrough

• Understand how AI systems are designed and applied in healthcare settings
• Build basic supervised learning models using real-world-style clinical data
• Interpret biomedical images and signals using AI-based approaches
• Evaluate AI model performance using standard metrics and validation techniques
• Recognize ethical challenges and regulatory requirements in medical AI
• Conceptualize end-to-end AI pipelines for healthcare applications
• Strengthen readiness for advanced research, internships, or industry roles in AI + healthcare domains

• Certificate of Completion from NanoSchool
• Access to curated Google Colab notebooks used in hands-on sessions
• Sample medical datasets for practice and learning
• Workshop presentation slides and reference materials
• AI in Medicine resource toolkit (reading + learning references)
• Post-workshop mentor support access (doubt resolution window)
• Career guidance and pathway insights in AI + Healthcare domains