
AI in Medicine: Foundations and Applications
Bridging Code and Care: Putting AI theory into medical practice.
Skills you will gain:
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.
Workshop Objectives:
What you will learn?
📅 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
Mentor Profile
Fee Plan
Important Dates
30 Oct 2026 Indian Standard Timing 3:30 PM
30 Sep 2026 to 02 Oct 2026 Indian Standard Timing 4:00 PM
Get an e-Certificate of Participation!

Intended For :
Career Supporting Skills
Workshop 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
