Machine learning: applications of artificial intelligence to imaging and diagnosis

• Published in: Biophysical reviews Vol. 11; no. 1; pp. 111 - 118
• Main Authors: Nichols, James A., Herbert Chan, Hsien W., Baker, Matthew A. B.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Biochemistry; Biological and Medical Physics; Biological Techniques; Biomedical and Life Sciences; Biophysics; Cell Biology; Dermatology; Diagnostic systems; Human performance; Language translation; Learning algorithms; Life Sciences; Machine learning; Membrane Biology; Nanotechnology; Radiology; Review; Computer vision; Microscopy; Dermatology; Imaging; Machine learning; Radiology; Artificial intelligence
• ISSN: 1867-2450; 1867-2469; 1867-2469
• DOI: 10.1007/s12551-018-0449-9

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.