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.

In this module, we will explore the fundamentals of programming, the reasons for choosing Python, installation on both Windows and Linux systems.

Understanding programming helps practioners to discover fresh insights and innovative research pathways, enhancing our ability to advance medical knowledge and theories.

We will learn essential concepts like variables and naming conventions, various operators, including conditional and relational ones. We will delve into conditional statements and loops to control program flow. Understanding functions, classes, and objects, as well as inheritance, will be a key focus. We’ll also cover parameters and initialization techniques. These topics are crucial for building a strong foundation in programming, enabling you to understand and implement solutions in Python.

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.

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 classes 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.

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.

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.

In this final module, We will explore the current state of AI in healthcare and its evolving role.

We’ll delve into the critical concepts of bias and variance tradeoff, understanding how to strike the right balance when designing healthcare AI systems to ensure accuracy and fairness. Additionally, we’ll provide an overview of topics covered in the previous module.