Machine learning: applications of artificial intelligence to imaging and diagnosis

James A. Nichols, Hsien W. Herbert Chan, Matthew A.B. Baker*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

291 Citations (Scopus)

Abstract

Machine learning (ML) is a form of artificial intelligence which is placed to transform the twenty-first century. Rapid, recent progress in its underlying architecture and algorithms and growth in the size of datasets have led to increasing computer competence across a range of fields. These include driving a vehicle, language translation, chatbots and beyond human performance at complex board games such as Go. Here, we review the fundamentals and algorithms behind machine learning and highlight specific approaches to learning and optimisation. We then summarise the applications of ML to medicine. In particular, we showcase recent diagnostic performances, and caveats, in the fields of dermatology, radiology, pathology and general microscopy.

Original languageEnglish
Pages (from-to)111-118
Number of pages8
JournalBiophysical Reviews
Volume11
Issue number1
DOIs
Publication statusPublished - 7 Feb 2019
Externally publishedYes

Fingerprint

Dive into the research topics of 'Machine learning: applications of artificial intelligence to imaging and diagnosis'. Together they form a unique fingerprint.

Cite this