Learn the applications of AI Tools, GenAI, and Healthcare Data to Build Real-World HealthTech Solutions. No Coding Required, Learn from IIT Kanpur Experts
What You’ll Learn
- Understand foundational AI and ML concepts in the context of healthcare
- Explore clinical datasets and learn how AI supports diagnostics, risk prediction, and monitoring
- Get introduced to explainability and generative AI tools like SHAP, LIME, and ChatGPT (demonstration only)
- Learn about ethical frameworks, health data standards (FHIR, HL7), and AI deployment strategies
- Develop and pitch an innovative, AI-enabled healthcare solution based on real industry needs
Key Features
Learn from a Healthcare-AI Expert
- Guided by Dr. Kashmira Ganji from IIT Kanpur, the course blends deep research, healthcare innovation, and emerging AI tools.
No-Code, Practice-Driven Learning
- Explore how AI works through demonstrations of tools like ChatGPT, SHAP, and healthcare data examples—designed for professionals with no coding background.
Capstone with Startup Thinking
- Pitch your own AI-based health-tech solution in the final project, simulating real-world innovation and application.
Skills You’ll Gain
- AI & ML Fundamentals
- Clinical Data Interpretation
- Healthcare Model Evaluation
- Explainable AI (XAI)
- Generative AI in Health
- AI Ethics & Compliance (FHIR/HL7)
- AI Solution Design & Pitching
- Cross-Functional Healthcare Communication
Tools Covered
- Microsoft Excel
- ChatGPT
- SHAP, LIME (XAI Tools)
- MIMIC-III, UCI Health Datasets
- FHIR & HL7 Integration Standards
- Wearable Data Simulators
- Health-tech Prototyping Tools (Figma/Balsamiq)
Target Audience
- Doctors, nurses, public health experts, and other healthcare professionals
- Medical and technology students
- Researchers, Administrators, Policymakers, Data scientists, and Healthcare Entrepreneurs
Curriculum
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Module 1: Foundations of AI in Healthcare
This module introduces the fundamental concepts of Artificial Intelligence, Machine Learning, and Deep Learning. Learners also explore the healthcare data ecosystem, including electronic health records (EHRs), medical imaging, genomics, and wearable data. Real-world business case examples illustrate how AI is applied across various data types in the healthcare domain.
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Module 2: Clinical Data and Machine Learning Techniques
Covers essential machine learning methods such as regression, classification, clustering, and model evaluation. The focus is on how these techniques are used to extract insights from health data. The module includes simple examples and case studies to build a solid understanding of ML in clinical contexts.
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Module 3: AI in Real-World Clinical Practice
Examines how AI is currently integrated into clinical workflows. Topics include AI in diagnosis, chronic disease management, hospital resource optimization, and decision support systems. Through real clinical examples and case studies, learners explore the challenges and opportunities of applying AI in everyday patient care.
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Module 4: Generative AI, Explainable AI & Agentic AI in Healthcare
Get introduced to cutting-edge AI tools and concepts transforming healthcare.
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- Use ChatGPT-like models for clinical summarization, patient education, and data entry
- Understand AI decisions using SHAP and LIME for clinician trust and transparency
- Explore Agentic AI and autonomous AI deployment in healthcare
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Module 5: AI for Wearables, Mobile Health, and Screening
Focuses on the application of AI in remote monitoring, mobile health platforms, and early screening tools. Learners explore how AI enhances wearable devices and mobile diagnostics, making healthcare more preventive, personalized, and accessible. Real-world product evaluations are included.
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Module 6: Ethics, Deployment, and AI Strategy
Covers the legal, ethical, and operational considerations of deploying AI systems in healthcare. Topics include patient data privacy, fraud detection, and compliance with industry standards such as HL7 and FHIR. Learners gain insights into how AI systems are introduced and scaled within healthcare organizations.
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Module 7: Innovation, Startups, and Pitching AI Solutions
Guides learners through the process of conceptualizing and presenting AI-based healthcare innovations. Topics include business model development, solution validation, stakeholder communication, and investor pitching. This module prepares learners to transition ideas into viable AI products or services.
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Module 8: Capstone Project – Designing an AI-First Healthcare Solution
Participants synthesize their learning by developing and pitching a complete AI solution targeting a real healthcare challenge—such as diagnosis, monitoring, or hospital efficiency. The capstone includes mentorship and feedback from faculty and peers to ensure practical and strategic alignment.
FAQs
Q-1. Who is this course ideal for?
Healthcare professionals, students, researchers, and innovators interested in applying AI to real clinical challenges.
Q-2. Is this course delivered online or offline?
It’s delivered 100% online via live weekend sessions with LMS and recorded access.
Q-3. Will I receive a certificate after completing the course?
Yes, you’ll receive a certificate from E&ICT Academy, IIT Kanpur upon successful completion.
Q-4. What is the class format?
Live weekend classes with faculty-led discussions, demos, and hands-on problem-solving.
Q-5. Do I need prior experience in AI or healthcare analytics?
No prior AI experience required—basic healthcare understanding is enough.
Q-6. What kind of projects will I work on?
You’ll work on real-world healthcare problems—like wearables, fraud detection, or AI diagnosis—and pitch a final solution.
Q-7. What makes this course different from others?
It combines GenAI, XAI, FHIR/HL7, and design thinking with no coding required—ideal for both tech and healthcare professionals.
Q-8. What support will I receive?
Live doubt-solving, LMS access, expert mentorship, and post-course community support.
Q-9. What roles does this course prepare me for?
Roles like Clinical AI Analyst, Digital Health Consultant, AI Project Manager, and HealthTech Innovator.
Instructor Profile
- Instructor Name: Dr. Kashmira Ganji,
- Designation: Post Doctoral Fellow, Department of Management Sciences, Indian Institute of Technology, Kanpur.
About: Dr. Kashmira is a researcher with expertise in healthcare technology, IoT applications, and digital transformation in supply chains. Her work explores how emerging technologies are improving healthcare delivery and operational efficiency. With several publications in reputed journals, she brings a practical, research-driven approach to teaching, bridging the gap between technology and healthcare systems.
