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September 30, 2026

Registration closes September 30, 2026

Mentor Based

AI in Medicine: Foundations and Applications

Bridging Code and Care: Putting AI theory into medical practice.

  • Mode: Virtual / Online
  • Type: Mentor Based
  • Level: Moderate
  • Duration: 3 Days (60-90 minutes each day)
  • Starts: 30 September 2026
  • Time: 4:00 PM IST

About This Course

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

  • Introduce fundamental concepts of AI and machine learning in healthcare applications
  • Explain supervised learning, deep learning, and generative AI use cases in medicine
  • Provide hands-on experience with medical datasets using Google Colab and real-world workflows
  • Explore biomedical image/signal analysis along with model evaluation and bias detection techniques
  • Highlight ethical, legal, regulatory aspects and career opportunities in AI-driven healthcare

Workshop 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

Who Should Enrol?

This workshop is designed for undergraduate and postgraduate students from Life Sciences, Biotechnology, Medicine, Engineering, and Computer Science backgrounds, as well as PhD scholars, faculty members, and academic researchers. It is also suitable for healthcare professionals, clinicians, and industry practitioners interested in integrating AI into biomedical and healthcare applications. Beginners with an interest in AI are welcome, and only basic Python familiarity is helpful but not mandatory.

Important Dates

Registration Ends

October 30, 2026
IST 3:30 PM

Workshop Dates

September 30, 2026 – October 2, 2026
IST 4:00 PM

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

Fee Structure

Student

₹1999 | $65

Ph.D. Scholar / Researcher

₹2999 | $75

Academician / Faculty

₹3999 | $85

Industry Professional

₹4999 | $100

What You’ll Gain

  • Live & recorded sessions
  • e-Certificate upon completion
  • Post-workshop query support
  • Hands-on learning experience

Need Help?

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(+91) 120-4781-217

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