Practical AI for Healthcare Professionals: Machine Learning with Numpy, Scikit-learn, and TensorFlow
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- Дата: 13-12-2021, 19:12
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Автор: Abhinav Suri
Издательство: Apress
Год: 2022
Страниц: 266
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB
Use Artificial Intelligence (AI) to analyze and diagnose what previously could only be handled by trained medical professionals. This book gives an introduction to practical AI, focusing on real-life medical problems, how to solve them with actual code, and how to evaluate the efficacy of these solutions. You’ll start by learning how to diagnose problems as ones that can and cannot be solved with AI or computer science algorithms. If you’re not familiar with those algorithms, that’s not a problem. You’ll learn the basics of algorithms and 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 library alogn with Numpy and Scikit-Learn are covered, too.
But how will physicians get up to speed and learn about this field, which has undergone so much rapid change in the past decade owing to the subtype of AI known as deep learning (DL)? In this new book, Abhinav Suri, a medical student at UCLA, has provided an outstanding primer for uninitiated clinicians. Abhinav has the perfect background for this: a double degree in computer science and biology from Penn, an MPH degree from Columbia, and additional experience leading medical scan AI research at the Perelman School of Medicine. In just seven chapters, he succinctly lays out the basics and delineates the limits and potential flaws of AI, the different types of machine learning (ML) algorithms and deep neural networks, and “snake oil” AI. We’ve needed such a book for the medical community to get grounded, not so that physicians can code, but rather to understand the power, nuances, and limitations as AI makes its way deeper into the practice of medicine.
The topics and projects covered 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 problems using modern libraries, such as TensorFlow. 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.
What You'll Learn:
Distinguish between problems that currently can and cannot be solved with AI
Master programming concepts not familiar to physicians, such as libraries, coding, and creating and training ML models
Perform dataset analysis with decision trees, SVMs, and neural networks.
Who This Book Is For:
Physicians and other healthcare professionals curious about AI and interested in leading medical innovation initiatives. Additionally, software engineers working on healthcare related projects involving AI.
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