Module 1: Introduction to Artificial Intelligence (AI) in Healthcare

• Clinical data sets: Data sources and types (structured, unstructured); Standards in data acquisition and management; Opportunities and challenges in data handling
• The role of smart and intelligent systems in clinical workflow: Traditional vs. intelligent systems in healthcare; concepts of intelligence and smartness
• Recent Inroads / Trends of AI in Healthcare: Landmark applications in healthcare

Module 2: Basics of Machine and Deep Learning (ML & DL)

• Introduction to AI: Definition, history, evolution, and applications
• Learning paradigms: Supervised (classification, regression), Unsupervised (clustering), Reinforcement Learning
• The ML Pipeline: Feature extraction, selection, dimensionality reduction; Model building, validation, and evaluation metrics
• Recent Trends: Generative AI, Foundational Models, ChatGPT
• Applications by Data Type:
  • Image-based – Retinal Scans (Diabetic Retinopathy), Chest X-ray (Pneumonia/Covid)
  • Tabular – EHRs (Covid, Sepsis)
  • Molecular – Genomics (Cancer), Structural Biology (Drug Discovery)
  • Text – Medical Q&A systems, ChatGPT Chatbots for Physical & Mental Health
• ML & DL Algorithms: Tree-based methods (decision trees, random forests), Neural Networks (MLP, SVM), Hierarchical Clustering, Deep Neural Networks (CNNs, RNNs, Transformers)

Module 3: Clinical Applications

• Case Study Framework: Clinical presentation & AI/ML role; Data prep & feature processing; Model building & evaluation; Results & inference
• Domains (any 4): Screening, Diagnosis, Prognosis, Treatment, Patient Management, Hospital Resource Management
• Specific Case Studies: Different data types (biosignals, images, tabular, molecular, textual) with relevant algorithms (tree-based, NN, DNN)
• Signal-based – ECG (cardiovascular diseases), EEG (sleep staging)

Module 4: Ethics and Governance of AI

• Data protection, privacy, anonymity, biases – Regulations and governance frameworks for AI as a medical device

Hands-on AI Projects (Optional)

• Building a basic machine learning model
• Simple AI projects relevant to healthcare