Artificial Intelligence for
 Medicine (Self-Paced)

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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 effectively. This includes:

– Medical records: Patient demographics, diagnoses, medications, lab results, imaging data.
– 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.

What Will You Learn?

  • AI for Medicine
  • Understanding How to develop an AI System

Course Content

Before You Start !
Please follow the instruction to make your learning easy and exciting.

  • Before you Start – Follow the Intructions
    00:00

AI for Medicine
In this module, we will delve into the fundamentals of AI, emphasizing its definition and the pivotal role of data in AI applications. Understanding how humans learn provides crucial insights into AI's design. We'll compare AI to traditional problem-solving methods, highlighting its advantages and limitations. Through real-world use cases, you'll discover how AI is revolutionizing various industries. Lastly, we'll explore the promising future of AI in healthcare, demonstrating its potential to transform patient care, diagnosis, and medical research, showcasing the profound impact of AI in shaping the healthcare industry.

Programming Basics

AI Space
In this module, we will dive into the realm of machine learning. We will understand what is machine learning, emphasizing its real-world applications in healthcare. We discuss the rise of deep learning and draw parallels between the human brain and neural networks, shedding light on how they interconnect. Additionally, we introduce the subdomains of computer vision and natural language processing within the AI space and their pivotal roles in healthcare. This topic underscores the importance of these concepts, equipping learners to comprehend and contribute to cutting-edge healthcare solutions, ultimately improving patient care and outcomes.

Machine Learning
In this module, We will deep dive into machine learning and data analysis techniques in healthcare. We'll start by exploring regression, understanding its fundamentals, and applying it in healthcare scenarios. Next, we delve into classification, learning how to classify patient data for diagnosis and treatment. We'll then explore clustering, a method to group clases for personalized healthcare. Additionally, we emphasize practical implementation and use cases in each technique to enhance your skills. Finally, we stress the significance of historical data (time series) in healthcare decision-making, as it enables trend analysis and predictive insights, crucial for improving patient care and healthcare system efficiency.

Advanced Machine learning in Medicine
In this module, We will explore neural networks and understand their significance in healthcare. Computer vision, image classification, segmentation, and detection will equip you with the tools to solve real-world healthcare problems. Practical implementations using Xray's and other opensource data will reinforce your skills. The module also covers natural language processing, its relevance in healthcare, and its synergy with computer vision, forming a new domain. You'll explore OCR use cases, enabling semi-automatic patient report entries. We will also explore how computer vision can have "attention" with the rise of transformative technologies, empowering you to tackle healthcare challenges with the latest advancements.

AI in Medicine & Drug Discover
In this module, We will explore the transformative role of AI in healthcare. We delve into how AI is revolutionizing drug discovery, from creating new drugs to the groundbreaking INS018_055 - the first drug developed with AI. We examine the immense potential of biotech companies harnessing AI for research and development. Furthermore, we discuss the future landscape of the medicine industry, considering AI's impact on diagnostics, treatment, and patient care. We also address the critical importance of randomized control trials in ensuring the safety and efficacy of AI-driven healthcare innovations. This topic equips learners with a holistic understanding of AI's significant and evolving role in healthcare.

Summary
In this final module, We'll provide an overview of topics covered in the previous module.

Project Work

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