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.
