Main description:

Practical AI for Healthcare Professionals

Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You'll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You'll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you'll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well.

Once you've mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images.

The topics covered in this book not only encompass areas of the medical field where AI is already playing a major role, but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to those problems. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients.

Contents:

Chapter 1: Introduction to AI and feasibility

* AI, ML, Big Data: What do the buzzwords mean?

* Defining a problem

* What can and cannot be solved

* Common algorithmic alternatives

* You think you need AI, now what?

* Data considerations for Healthcare & Patient Privacy

* Cautionary tales of AI Snake Oil in Healthcare

Chapter 2: AI in theory

* Classification problems in the field of healthcare

* Decision trees

* Logistic regression

* Support vector ,achines

* Neural Networks and Deep Learning

* Convolutional Neural Networks

* Evaluation metrics for AI-driven diagnostic tools

Chapter 3: Overview of Programming

* Introduction to Python and environment set up

* Control Structures & Loops

* Data structures

* Functions

* File I/O

* Classes

* Packages/Libraries

* Numpy & Matplotlib

Chapter 4: Project #1 ML & Diabetes

* Problem overview and why ML might be the best

* Introduction to scikit-learn

* Data Pre-processing

* Try 1: Decision Trees

* Try 2: k Nearest Neighbors

* k-fold Cross Validation

* Takeaways

Chapter 5: Project #2 Neural Networks & Heart Disease

* Problem overview and why neural networks might work

* Introduction to keras

* Data Pre-processing

* Model design and implementation

* Measure Efficacy

* Takeaways

Chapter 6: Project #3 CNNs & Brain Tumor Detection

* Problem overview

* Overview of segmentation problems and Mask-RCNN

* Data Pre-processing & Working with MRI images

* Data Augmentation

* Model design and implementation

* Measure Efficacy with Dice Score and AP metrics

* Takeaways

Chapter 7: The Future of Healthcare and AI

* Review of book

* Problems in Medical AI: Data Issues

* Medical Problems waiting to be solved

* Misconception of the "death" of traditional Radiology

* Ethical AI in medicine

* Next steps