Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge

TL;DR

This paper offers a gentle, nontechnical introduction to machine learning tailored for physicians, focusing on medical applications, common algorithms, challenges, and future impacts in healthcare.

Contribution

It provides an accessible overview of machine learning concepts and their relevance to medical professionals, emphasizing practical examples and future outlooks.

Findings

Machine learning algorithms can address various healthcare data analysis tasks.

There are open challenges and limitations in applying machine learning to medicine.

Machine learning has the potential to significantly impact medical practice and research.

Abstract

Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.