Clinical Applications of Machine Learning.

ABSTRACT:

Objective

This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users.

Background

As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies.

Methods

This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinforcement learning models, outlines result interpretation, and provides references for in-depth information about each analytical framework.

Results

This review introduces inte

SUBMITTER: Mateussi N 

PROVIDER: S-EPMC11191915 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature