Machine learning: applications of artificial intelligence to imaging and diagnosis
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...
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| Published in | Biophysical reviews Vol. 11; no. 1; pp. 111 - 118 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1867-2450 1867-2469 1867-2469 |
| DOI | 10.1007/s12551-018-0449-9 |
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| 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. |
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| AbstractList | 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.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. 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. |
| Author | Baker, Matthew A. B. Herbert Chan, Hsien W. Nichols, James A. |
| Author_xml | – sequence: 1 givenname: James A. surname: Nichols fullname: Nichols, James A. organization: Laboratoire Jacques-Louis Lions, Sorbonne Université – sequence: 2 givenname: Hsien W. surname: Herbert Chan fullname: Herbert Chan, Hsien W. organization: Centenary Institute, The University of Sydney, Department of Dermatology, Royal Prince Alfred Hospital – sequence: 3 givenname: Matthew A. B. orcidid: 0000-0002-5839-6904 surname: Baker fullname: Baker, Matthew A. B. email: matthew.baker@unsw.edu.au organization: School of Biotechnology and Biomolecular Sciences, University of New South Wales |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30182201$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
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| Title | Machine learning: applications of artificial intelligence to imaging and diagnosis |
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