Executive Programme in AI-Driven Healthcare Transformation

Program Description

•    Identify Opportunities: Assess how AI, Generative AI, and Agentic AI are reshaping diagnosis, treatment, patient care, and healthcare operations, and identify where these technologies can create meaningful value.
•    Make Sense of Data: Interpret healthcare data and AI-driven insights to support more confident clinical, operational, and patient-care decisions.
•    Prioritise Practical Use Cases: Evaluate AI applications across diagnostics, patient monitoring, drug discovery, and care workflow optimisation with a clear view of impact, feasibility, and responsible use.
•    Augment Clinical Knowledge: Apply Generative AI tools to improve clinical documentation, patient summaries, communication, reporting, and knowledge management.
•    Create AI-Enabled Care Workflows: Design AI-enabled healthcare workflows that improve efficiency, strengthen care coordination, and enhance patient experience while maintaining safety and trust.
•    Transform Strategy into Action: Develop practical strategies for implementing and scaling AI solutions within healthcare organisations while aligning with patient safety, clinical priorities, and organisational readiness.

Key Highlights

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100% Live Online Learning with Domain Experts: Learn through live online sessions with domain experts and select IITMP faculty sessions

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AI- First Healthcare Curriculum: Learn AI through clinical, operational, patient-care, and research contexts.

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Select live Masterclasses by Top IITM Pravartak Faculty: Learn directly from faculty at IITM Pravartak

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Built for Healthcare Professionals: Develop AI understanding without needing to become an AI engineer.

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Generative AI in Healthcare: Simplify clinical information, communication, and diagnostic reporting.

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Agentic AI in Healthcare Workflows: Improve care coordination and operational responsiveness with AI agents.

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Hands-On Exposure to Relevant AI Tools: Gain practical exposure to tools for healthcare AI application.

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Certificate from IITM Pravartak: Earn a digital certificate upon successful programme completion.

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2-Day Campus Immersion: Two-day campus immersion event at IIT Madras Research Park (Optional)

Learning Format

Online

Duration

5 Months

Certified by

IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras and
Emeritus

Program Fee

(For Indian Residents)

Programme Fee: INR 154900 + GST

Application Fee: INR 1200 + GST 

Education Qualification

Graduates (10+2+3) from a recognized university (Medical Graduates Preferred) as on the programme start date

Lead Faculty

Mr. Laxminarayan G, Guest Faculty, IITM Pravartak


Laxminarayanan G. is a Strategic AI and Generative AI leader with over two decades of experience driving large-scale digital transformation and automation-led value across the BFSI, CPG, and Technology sectors. A recognised TEDx speaker and trusted advisor to institutions such as ISRO and the IIMs, he has played a key role in influencing CXO-level AI strategy and enterprise adoption globally.
He has built and scaled world-class AI and GenAI practices, delivering over $20M in business impact. His expertise spans GPT-4, Azure OpenAI, RAG systems, and enterprise AI platforms. A distinguished mentor to ISRO, Intel OneAPI Innovator, and visiting faculty at IIM Lucknow, IIM Kozhikode, and IIM Indore, he actively teaches and mentors in AI, analytics, digital transformation, and strategic growth.

Learning Module

  • Module 1: The Digital Revolution in Healthcare

    • How healthcare is digitising

    • New tech in diagnosis and treatment

    • How AI and robotics are used in care

  • Module 2: How AI Works in Healthcare

    • Key AI terms explained

    • How to frame a problem for AI

    • What data an AI model needs

    • Basics of evaluating an AI model

    • Common deployment challenges

    • Designing an AI-first healthcare idea

  • Module 3: Biomedical Informatics and Healthcare Data

    • What biomedical data is

    • Types of healthcare data

    • How health systems share and use data

    • Simple queries for healthcare data

  • Module 4: AI Models and Healthcare Applications

    • How AI learns from healthcare data

    • Supervised and unsupervised learning in healthcare

    • Pattern recognition in clinical and imaging data

    • Common healthcare AI applications

    • Understanding model outputs and predictions

    • Limitations of AI models in healthcare

  • Module 5: Use of Hybrid Clouds in Healthcare

    • Why hospitals use cloud systems

    • How AI improves admin tasks

    • Cloud strategies with AWS, Azure and Google

  • Module 6: AI in Clinical Decision Making

    • Predicting disease risk

    • AI-based patient scoring

    • AI-assisted diagnosis and monitoring

  • Module 7: AI for Healthcare Operations and Patient Flow

    • AI for patient flow and bed management

    • AI for scheduling and operations

    • Case example: AI in emergency call centres

  • Module 8: Role of AI in Drug Discovery

    • Why drug discovery is hard

    • How AI speeds discovery

    • AI across the drug development cycle

  • Module 9: Generative AI for Clinical Documentation and Summaries

    • Drafting clinical notes

    • Summarising patient records

    • Creating discharge summaries

  • Module 10: Generative AI for Patient Communication and Education

    • Generating instructions

    • Translating medical terms

    • Simplifying patient explanations

  • Module 11: Generative AI in Diagnostics and Reporting

    • Drafting structured reports

    • Pattern-based prompting

    • Assisting specialist reviews

  • Module 12: Safe and Responsible Use of Generative AI

    • Avoiding hallucinations

    • Validating AI outputs

    • Safe-use guidelines

  • Module 13: Introduction to AI Agents in Healthcare

    • What AI agents are

    • Where agents fit in hospital workflows

  • Module 14: Workflow Automation with AI Agents

    • Automating routine tasks

    • Follow-ups and reminders

    • Care coordination tasks

  • Module 15: Agents for Operations and Resource Management

    • Scheduling agents

    • Resource allocation agents

    • Patient routing agents

  • Module 16: Implementing AI in Clinical Practice

    • How clinical AI is deployed

    • Regulation and ethics

    • What makes AI adoption successful

    • AI in monitoring and wearable tech

    • LLMs for clinical workflows

    • AI in treatment planning

  • Module 17: Human–AI Collaboration in Healthcare

    • Where AI supports clinicians

    • Combining human and AI judgment

    • Building trust in AI tools

    • The human-in-the-loop model

    • Reducing errors through collaboration

    • Real hospital examples

  • Module 18: The Potential for Bias and Harm in AI

    • Common sources of bias

    • How to reduce bias

    • Safe deployment practices

  • Module 19: Building AI-Ready Healthcare Systems

    • Steps to adopt AI in hospitals

    • How to measure AI ROI

    • Global examples of AI systems

Designing AI-Driven Healthcare Solutions

  • Choose a real healthcare problem

  • Design and test an AI intervention

  • Present solution and receive feedback



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