Machine learning in medicine: what clinicians should know

Abstract

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.

Keywords: Algorithms; artificial intelligence; deep learning; machine learning; neural networks.

Figure 1

Flowchart shows the diagnosis of diabetes mellitus when fasting glucose is not ≥7.0 mmol/L or casual/2-hr post-challenge glucose is not ≥11.1 mmol/L.[13]

Figure 2

Diagram shows single layer of computational neurons.[20]

Figure 3

Diagram shows addition of more layers to make a neural network.[21]

Figure 4

Diagram shows the anatomy of a decision tree.^[3]

Figure 5

Graph shows data that is not traditionally linearly separable can be projected into a higher dimensional space, which can make it linearly separable. [Adapted from: Alpaydin E. Introduction to Machine Learning. Cambridge, MA: MIT Press; 2004]