Machine Learning in Healthcare

By Hafsa Habehh, Suril R. Gohel. Current Genomics, 2021

338 citations recorded by the source corpus.

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b50d99925701a88ce998323af1307b92a5b87258

Citations in the corpus

• A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare (2023, neutral): “Machine learning encompasses the use of data and algorithms to mimic the human decision-making process, with the overall aim of improving its accuracy [3] and is one of the most common forms of AI being used in different areas of healthcare.”