About the Ai In Healthcare Course
Program Highlights
Course Curriculum
Module 1: Day 1: Introduction to AI in Healthcare
- Explore the definition and significance of AI in healthcare.
- Identify core AI techniques such as ML, NLP, and computer vision.
- Discuss data privacy, bias, and ethical considerations.
Module 2: Day 2: Applications and Tools
- Implement AI‑powered diagnostic models for medical imaging.
- Build predictive analytics solutions for patient outcomes.
- Utilize leading AI libraries and platforms (TensorFlow, Keras, PyTorch, IBM Watson Health, Google Health AI).
Module 3: Day 3: Advanced Topics & Hands‑On Project
- Apply AI to genomics for personalized medicine and drug discovery.
- Develop a complete AI solution to predict patient readmission risk.
- Explore emerging trends such as AI‑driven mental health, surgical robotics, and telemedicine.
Tools, Techniques, or Platforms Covered
Keras
PyTorch
IBM Watson Health
Google Health AI
Real-World Applications
- Apply AI in Healthcare skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical healthcare competencies
- Solve industry-relevant problems using AI in Healthcare methodologies and tools
- Contribute to open-source projects and collaborative research in healthcare
- Prepare for competitive examinations, interviews, and professional certifications in healthcare
Who Should Attend & Prerequisites
- Industry‑recognized e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and real healthcare datasets
- Dedicated expert mentorship and doubt resolution
Prerequisites:

AI in Autonomous Vehicles
Robotics and Automation in Agricultural Biotechnology Course
Sonochemical and Microwave Methods for Nanomaterials Production
Nanomaterials for Automobiles
AI-Powered Econometric Forecasting & Causal Inference
R and Python for Bioinformatics
Internet of Things (IoT) in Manufacturing
Synthesis of Carbon Dots from Kitchen Waste
SQL for Data Analysis
AI for Quantitative Pathology and Biomarker Analysis
Machine Learning for Bioinformatics: Basics 






