Executive Programme for AI in Healthcare (Batch 2)
Features
Programme Overview
Artificial Intelligence (AI) is reshaping healthcare-from early diagnosis to personalized care. IIT Delhi’s 6-month Executive Programme for AI in Healthcare is designed for professionals looking to lead this transformation. Through 80 hours of live online sessions, including fundamentals and clinical application of AI you'll gain practical skills in AI (Machine Learning (ML) & Deep Learning (DL)), work hands-on with real clinical datasets, and learn to build and deploy predictive models. With expert guidance from IIT Delhi , the programme includes a capstone project and an optional two-day campus immersion. No prior coding experience is required - just a drive to innovate in healthcare
₹ 1,50,000 + 18 % GST
Registration Closed
Class Schedule
Saturday: 6:00 PM – 7:30 PM and Sunday: 11:00 AM – 12:30 PM
Eligibility Criteria
Any graduate professional working in industry and academia with area relevant to AI in healthcare
Programme Highlights
E-Certificate Of Successful Completion From CEP, IIT Delhi
Led By Experts From IIT Delhi
Hands-On, Real-World Learning
Capstone Project & Campus Immersion
Reputation & Recognition
Programme Modules
Fundamentals of AI, Machine Learning, and Deep Learning (non-technical explanation) Prompt Engineering and Applications of AI in Healthcare.
Supervised vs. Unsupervised Learning, Key ML/DL algorithm walkthrough (Linear Regression, Decision Trees, Clustering, logistic regression, SVM, Neural Network (NN), Deep NN, Convolution NN, LLM, Generative AI etc.), When to use what? (Healthcare use cases)
Python and MATLAB basics (Variables, Functions, Libraries), Data handling with Pandas & Numpy, Assignments: Analyze sample healthcare CSV data file, signal and images.
Overview of EMR data, medical imaging data, histopathology images, physiological signals, genomics data, IoT data, Structure of Indian hospital data (practical exposure). Case study: IBM Watson for Oncology's deployment success & limitations in India
Key applications and challenges of healthcare data (Opportunity for problem statements)
Overview of public healthcare dataset such as MIMIC-III dataset, BraTS challenge dataset, etc.
Healthcare data anonymization, pre-processing, data curation, data cleaning, missing value handling, data normalization, feature engineering, data augmentation, data split for training and testing, qualitative vs quantitative analysis, accuracy evaluation metrics
Healthcare data ethics & compliance: HIPAA, GDPR, DISHA
Introduction to big data and big data analytics using frameworks such as Apache Spark.
Assignment: Related to the Preprocessing sample healthcare dataset
Development / Implementation and optimizations of ML models such as Logistic Regression, Random Forest, SVM, Neural Network, Example Case Studies
Development / Implementation and optimizations of DL models such as Convolution Neural Networks (CNN), Recurrent Neural Networks, Generative Adversarial Networks (GANs), Transformer for various tasks such as segmentation, classification, prediction, synthetic image generation.
Example Project Assignments Options (Python/MATLAB only):
CNN model for segmentation of a pathology on medical images such as X-Ray, MRI, CT, etc.
ML model for imaging-based diagnosis
ML model for physiological signal-based diagnosis
CNN model for diagnosis classification of images/disease
Image Synthesis using GAN
IoT sensors, data streams, real-time AI monitoring. Case study: AI in diabetic foot ulcers, smart watches
Building AI-powered decision support for doctors. Case study: Apollo CDSS, NHS AI tools
Automating admin tasks (billing, triage, discharge), RPA tools intro (UiPath, Automation Anywhere overview)
Generative AI in Healthcare: LLMs, no-code tools, prompt engineering, radiology use-cases, (Application of AI in radiology, genomics, surgery, pharma, etc) regulatory basics AI-powered chatbots, virtual consultations. Case study: Niramai breast cancer AI screening.
Create AI-powered healthcare dashboards (Streamlit or MATLAB GUI), Deploy models on cloud (GCP/AWS intro)
How AI plugs into HIS workflows; Data visualization using Streamlit or MATLAB only.
Case Study: Streamlit-based diabetes risk dashboard used by clinical trial teams
Time-series modeling for COVID-like prediction, Geo-mapping disease spread (India datasets)
Using AI insights for healthcare planning.
Case study 1: AI for malaria & dengue surveillance.
Case study 2: AI in malaria surveillance & mapping in OdishaCapstone Projects (Group Project)
Applied AI for healthcare – develop ML/DL model or AI dashboard using Python/MATLAB, final presentation & evaluationExpert Roundtable
Healthcare innovation trends, regulatory talks, med-tech career guidanceAssessments
Quizzes, Assignments, Capstone Projects, Mid Term and End Term reports
Learning Outcomes
AI Foundations: Gain a clear understanding of AI/ML/DL fundamentals, including supervised and unsupervised learning methods, with healthcare-focused examples.
Data and its Management: Learn about healthcare data such as EMR/EHR data, medical images, histopathological images, physiological signals, genomics, and IoT sensor data. Learn about applications and challenges of healthcare data. Develop skills to access, preprocess, visualize and analyze healthcare data
Ethics & Compliance in Healthcare Data: Learn about ethics and compliance related to healthcare data.
Predictive Modeling Techniques: Build and evaluate predictive models using machine learning and deep learning tailored to disease diagnosis, treatment planning and risk scoring.
AI Applications in Healthcare: Explore AI use cases in radiology, pathology, physiology, ophthalmology, real-time patient monitoring with IoT devices, and clinical decision support systems.
Model Deployment and Integration: Learn to deploy AI models through dashboards and cloud platforms, and integrate them into healthcare information systems (HIS).
Tools
Matplotlib
Python
Pandas
Tensor Flow
Spa Cy
Num Py
Machine Learning
NLTK
Scikit-learn
AWS
3 D Slicer
Apache Spark
FIJI
Google Cloud Platform
Hugging Face
Keras
MATLAB
Seaborn
Streamlit
Programme Coordinator
Professor Anup Singh
Professor
Centre for Biomedical Engineering
Indian Institute of Technology Delhi
Dr. Anup Singh is a leading researcher and educator in biomedical imaging, currently serving as faculty at the Centre for Biomedical Engineering, IIT Delhi, and the Department of Biomedical Engineering, AIIMS New Delhi. He is also an associate faculty at Yardi School of AI at IIT Delhi. With a PhD from the Department of Mathematics and Statistics at IIT Kanpur and postdoctoral experience from the Department of Radiology at the University of Pennsylvania, Dr. Singh brings over a decade of expertise in advanced MRI techniques, quantitative imaging, machine learning & deep learning for medical imaging, along with an experience in the development of MRI- compatible devices. He has authored 80+ peer-reviewed papers (including in Nature Medicine), holds 5 US/ Indian patents, and is actively involved in translational biomedical imaging research.
Professor Amit Mehndiratta
Professor
Centre for Biomedical Engineering
Indian Institute of Technology Delhi
A physician-engineer, Dr. Amit Mehndiratta holds an MBBS from Dr. MGR Medical University, a Master's from IIT Kharagpur, and a D.Phil. from the University of Oxford. He currently serves as joint faculty at IIT Delhi and AIIMS New Delhi, focusing on neuro-assistive technologies and biomedical imaging. His past affiliations include Harvard Medical School, Massachusetts General Hospital, and the German Cancer Research Center. He has received multiple awards, including the SERB Technology Translation Award and the Erics- son Innovation Award. Dr. Mehndiratta leads CARE-DAT, a CoE in Assistive Technology supported by ICMR.
Programme Sample Certificate
Participation Certificate: Awarded with a minimum of 50% attendance and less than 40% overall marks.
Successful Completion Certificate: Awarded with a minimum of 50% attendance and 40% or above overall marks.
The above e-certificate format is for illustrative purposes only and may be modified at the discretion of IIT Delhi.
Only e-certificates will be issued and will be provided by CEP, IIT Delhi.
The organizing department for this programme is the Centre for Biomedical Engineering, IIT Delhi.
Installment Schedule
Programme Fees: 1,50,000 + 18 % GST
Installment | Installment Date | Amount (₹) |
I | Within 3 Days of the offer letter. | ₹ 75,000+GST |
II | 31st July, 2026. | ₹ 75,000+GST |
Note:
*All fees should be submitted in the IITD CEP account only, and the details will be shared post-selection.
*The receipt will be issued by the IIT Delhi CEP Account for your records, which can be downloaded from the CEP Portal.
*GST @ 18% will be charged extra in addition to the fee.
Refund Policy
Candidates can withdraw within 15 days from the programme start date. A total of 80% of the total fee received will be refunded. However, the applicable tax amount paid will not be refunded on the paid amount.
Candidates withdrawing after 15 days from the start of the programme session will not be eligible for any refund.
If you wish to withdraw from the programme, you must email cepaccounts@admin.iitd.ac.in and cepdelhi@digivarsity.com, stating your intent to withdraw. The refund, if applicable, will be processed within 30 working days from the date of receiving the withdrawal request.
Frequently asked questions
This 6-month programme enables professionals to apply artificial intelligence in health care by working with real clinical data, building intelligent models, and developing AI-driven solutions for clinical and public health challenges. Delivered through live weekend sessions, hands-on projects, and expert mentorship from IIT Delhi faculty, it blends academic insight with real-world applicability.
Designed for professionals in healthcare, life sciences, IT, and related fields, this AI in healthcare course is especially relevant for clinicians, researchers, and technology leaders aiming to solve healthcare challenges using AI. Whether you're in clinical practice or healthcare management, the course offers an opportunity to explore impactful AI applications.
No. The programme welcomes graduate professionals from diverse academic and professional backgrounds who work in or aspire to enter the AI healthcare industry. The curriculum includes foundational modules and structured guidance, making it accessible for participants without prior technical or medical expertise.
Learning is reinforced through periodic quizzes, clinical AI tasks, mini viva sessions, and capstone project reviews. The final assessment covers both theory and application via multiple-choice questions (MCQs), an oral viva, and a practical coding test using Python or MATLAB. All evaluations are conducted online with proctoring to ensure academic integrity. Foundational support is provided for those new to coding.
The two-day campus immersion offers a valuable chance to engage face-to-face with IIT Delhi faculty and peers. However, travel, accommodation, and related costs are not included in the programme fee and must be borne by the participants.
Yes. You will connect with a diverse cohort of healthcare, technology, and data science professionals through live sessions, collaborative projects, and the two-day campus immersion - building strong professional relationships in the evolving AI in healthcare space.
Participants who meet the evaluation requirements will be awarded an e-Certificate of Successful Completion from CEP, IIT Delhi. Those who fulfill the minimum attendance but not the evaluation criteria will receive a Certificate of Participation. This certification validates your skills in AI for healthcare and enhances your profile in the growing healthcare AI sector.