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Artificial Intelligence for Medicine
Course Content
Before You Start !
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Before you Start – Follow the Intructions
00:00
AI for Medicine
Programming Basics
AI Space
Machine Learning
Advanced Machine learning in Medicine
AI in Medicine & Drug Discover
Summary
Project Work
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About Course
Artificial Intelligence for Medicine
“AI for Medicine” course is designed to provide Healthcare professionals with a practical understanding of artificial intelligence (AI) in healthcare.
AI, or Artificial Intelligence, is like creating smart machines that can think and make decisions on their own. It’s making computers learn to solve problems and make choices, somewhat like how humans do.
Imagine a robot doctor that can learn from tons of medical data and help diagnose diseases like a seasoned physician. That’s AI in a nutshell! It’s like teaching a computer to think and act intelligently, making decisions based on what it has “learned” from data.
How AI Learns Compared to Human Learning ?
AI learns by looking at a lot of examples and figuring out patterns. It’s a bit like how you learn to recognize cats after seeing many pictures of them.
AI uses algorithms, which are like step-by-step instructions, to learn from data. While humans learn through experience and intuition, AI learns through analyzing lots of data and finding patterns in it.
Why is it Important to Learn AI in 2024?
AI is revolutionizing almost every field, and healthcare is no exception.
It’s helping doctors diagnose diseases earlier, predict patient outcomes, and even develop personalized treatments. In 2024, understanding AI is like knowing basic computer skills — it’s becoming essential for any profession!
AI also assists in analyzing vast amounts of medical data quickly, leading to more informed decision-making and improved patient outcomes.
How to Create an AI System?
1. Problem Statement:
This is where you define the specific healthcare challenge you want AI to address. For example, you
could aim to:
– Improve early cancer detection by analyzing mammograms.
– Predict the risk of heart disease based on patient demographics and medical history.
– Personalize medication dosages for patients with chronic conditions.
– The key is to choose a well-defined, achievable problem with potentially significant medical impact.
2. Data Collection:
Think of data as the fuel for your AI engine. You need high-quality, relevant data to train your model
– Research findings: Published studies and datasets related to your chosen problem.
– Real-world data: Sensor data from wearable devices, medical images from hospitals, etc.
Data collection raises ethical considerations, so ensure patient privacy and informed consent are
always respected.
3. Model Training:
Here’s where the magic happens! You choose an AI algorithm (e.g., deep learning, machine learning)
based on your data and problem. Imagine feeding your data to a sophisticated learning machine that
discovers patterns and relationships hidden within it. The more data you feed, the better the model
learns.
Model training requires technical expertise and computing power. You can collaborate with AI
specialists or learn how to do this step effectively for training your model.
4. Testing & Refinement:
It’s not enough to blindly trust your AI. You need to test it on new data to see how well it performs. This
involves:
– Validation: Testing on a small portion of your data to see if the model generalizes well to
unseen examples.
Based on the test results, you may need to refine your model by adding more data, or even switching
to a different algorithm.
5. Deployment & Integration:
Implement the AI system for widespread use. Develop user interfaces or connect it to medical
systems, and train healthcare professionals on how to use it.
