Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
It addresses the need for physicians to understand machine learning amidst increasing data in medical research, but it is incremental as it offers an overview without new methods or results.
The paper provides a nontechnical introduction to machine learning tailored for physicians, explaining common algorithms and tasks with healthcare examples, and discusses open challenges and impacts in medicine.
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.