Indian Institute of Technology Kanpur

Artificial Intelligence in Healthcare

Indian Institute of Technology Kanpur

Healthcare

Application Deadline Approaching

Last date to apply: December 18, 2026 · Program starts on December 19, 2026

About Programme

Master AI Tools, GenAI, and Healthcare Data to Build Real-World HealthTech Solutions. No Coding Required, Learn from IIT Kanpur Experts

Certificate from EICTA consortium upon successful completion
35+ hours of live and practical learning with healthcare-focused AI tools
Capstone project to pitch an AI-based healthcare solution with expert mentorship

What you'll learn

Build Strong Foundations in Artificial Intelligence and Machine Learning for Healthcare Applications.
Explore Clinical Datasets, Model Evaluation Techniques, and Real-World AI Deployment in Healthcare.
Apply Explainable and Generative AI Tools Including SHAP, LIME, and ChatGPT for Healthcare Use Cases.
Understand Healthcare AI Ethics, FHIR/HL7 Standards, and Digital Health Integration.
Design, Develop, and Present AI-Driven Solutions for Real-World Clinical Challenges.

Skills you'll gain

AI & ML FundamentalsClinical Data InterpretationHealthcare Model EvaluationExplainable AI (XAI)Generative AI in HealthAI Ethics & Compliance (FHIR/HL7)AI Solution Design & PitchingCross-Functional Healthcare Communication

Programme Content

Foundations of AI in Healthcare

Key Topics

  • Introduction to AI, ML, and DL in healthcare context
  • Evolution and strategic role of AI in healthcare systems
  • Healthcare data ecosystem: EHR, EMR, imaging, genomics
  • Case study: structured clinical data interpretation
  • AI Tools: Notebook LM, OpenRouter

    • Learning Outcome

  • Participants will develop conceptual clarity on AI fundamentals.
  • Understand healthcare data architecture.
  • Interpret how AI integrates into modern clinical ecosystems.

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    Machine Learning Methods for Clinical Applications

    Key Topics

  • Supervised learning: regression, classification, decision trees
  • Unsupervised learning: clustering, PCA, feature engineering
  • Model evaluation metrics: ROC-AUC, F1-score, precision-recall
  • Clinical case study on model performance interpretation
  • AI Tools: TensorFlow Playground, Collab.research

    • Learning Outcome

  • Interpret machine learning models in clinical context.
  • Evaluate clinical performance metrics.
  • Understand predictive modelling relevance in healthcare decision-making.

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    Applied AI in Diagnostics and Clinical Workflows

    Key Topics

  • AI in diagnostics: radiology, pathology, dermatology
  • Risk prediction models: chronic disease, ICU, emergency care
  • End-to-end clinical workflow analysis
  • Integrated healthcare case studies
  • AI Tools: Numerous AI tools, Google Workspace Sheets AI

    • Learning Outcome

  • Understand real-world diagnostic AI systems.
  • Analyze risk monitoring frameworks.
  • Evaluate how AI optimises complete clinical workflows.

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    Generative AI, Explainability, and Trust in Healthcare

    Key Topics

  • Generative AI and LLMs: summarization, documentation, data entry
  • Explainable AI: SHAP, LIME, XAI dashboards
  • Trust, bias, and generalizability in clinical AI
  • Applied case study on ethical model deployment
  • AI Tools: Granola, ChatGPT, Custom GPTs, Gemini Dashboards, Claude Agent

    • Learning Outcome

  • Evaluate generative AI use cases in healthcare.
  • Interpret explainability tools for model transparency.
  • Assess bias and trust considerations in clinical AI deployment.

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    AI in Preventive Care and Digital Health Systems

    Key Topics

  • AI and IoT in chronic disease management
  • Wearables and smart health data integration
  • Screening and early detection models
  • Real-world product and solution analysis
  • AI Tools: Google AI Studio, Bolt, Lovable

    • Learning Outcome

  • Understand AI-driven preventive care systems.
  • Analyze digital health technologies for early detection.
  • Evaluate long-term disease monitoring frameworks.

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    Governance, Compliance, and Deployment Strategies

    Key Topics

  • Ethics and governance: consent, fairness, accountability
  • Fraud detection and hospital process optimisation
  • AI deployment frameworks: FHIR, HL7 standards
  • Clinical case discussions on compliance readiness
  • AI Tools: OLLAMA, “There is an AI for that”

    • Learning Outcome

  • Understand regulatory frameworks for healthcare AI.
  • Evaluate ethical safeguards in AI systems.
  • Apply deployment standards in healthcare environments.

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    Innovation, Feasibility, and Healthcare Entrepreneurship

    Key Topics

  • Building AI-first healthcare startups
  • Feasibility assessment and impact evaluation
  • AI proposal validation and scalability analysis
  • Crafting compelling pitches and storytelling frameworks
  • AI Tools: WAPI (VAPI)

    • Learning Outcome

  • Evaluate AI opportunities in healthcare innovation.
  • Assess feasibility and scalability.
  • Design impactful healthcare AI proposals.

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    Capstone Project – AI-Enabled Healthcare Solution Design

    Key Focus Areas

  • Problem identification and clinical relevance mapping
  • AI solution framework and workflow architecture
  • Impact assessment and feasibility validation
  • Structured presentation and expert feedback

    • Learning Outcome

  • Synthesize course concepts into a practical solution.
  • Design and validate an AI-enabled healthcare system.
  • Professionally present a solution aligned with real-world clinical challenges.

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    Program Certificate

    Earn a career certificate

    Earn an industry-recognized certificate from the E&ICT Academy in artificial intelligence in healthcare courses and showcase your expertise in AI in healthcare specialization, digital health, and real-world healthcare AI applications.

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    Artificial Intelligence in Healthcare | IIT Kanpur