Demystification of AI-driven medical image interpretation: past, present and future.
The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-lo...
| Publicado en: | European Radiology Vol. 29; no. 3; pp. 1616 - 1625 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images review tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134415300&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134415300 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Mar2019 vid: 29 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134415300 134415300 NLM30105410 134415300 10.1007/s00330-018-5674-x NLM30105410 134415300 ppf: 1616 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Demystification of AI-driven medical image interpretation: past, present and future. aug: au: Savadjiev, Peter Chong, Jaron Dohan, Anthony Vakalopoulou, Maria Reinhold, Caroline Paragios, Nikos Gallix, Benoit affil: Department of Diagnostic Radiology, McGill University, Montreal, QC, Canada sug: subj: Radiographic Image Interpretation, Computer-Assisted Methods Artificial Intelligence Trends Technology, Radiologic Trends Algorithms Forecasting ab: The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it. KEY POINTS: • Artificial intelligence (AI) research in medical imaging has a long history • The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods. • A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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