About Workshop
AI in Medicine: Foundations and Applications is a structured, hands-on NanoSchool virtual workshop designed to introduce participants to the rapidly evolving intersection of artificial intelligence and modern healthcare. The program bridges foundational machine learning concepts with real-world clinical and biomedical applications, enabling learners to understand how AI systems are built, evaluated, and deployed in medical environments.
Through guided coding sessions, case-based learning, and practical demonstrations using Google Colab, participants will gain exposure to AI-driven diagnosis, biomedical data analysis, and generative AI pipelines in medicine. The workshop also emphasizes responsible AI use, including ethics, safety, and regulatory considerations in healthcare systems.
Aim
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.
What Participants Will Learn
Structure
📅 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
Important Dates
Registration Ends
3:30 PM
Workshop Dates
2026-10-19
4:30 PM
4:30 PM
What You Will Gain
- 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
Outcomes
- 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
Who Should Attend
Deliverables
- 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