Programme Content
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 Odisha
Capstone Projects (Group Project)Applied AI for healthcare – develop ML/DL model or AI dashboard using Python/MATLAB, final presentation & evaluation
Expert RoundtableHealthcare innovation trends, regulatory talks, med-tech career guidance
AssessmentsQuizzes, Assignments, Capstone Projects, Mid Term and End Term reports
