Abstract
In this pilot study data gathered from interviewing specialists in radiology is combined with an assessment of the way machine learning metrics are used in studies of radiological work. It argues that situated context of use should be an important contributor to the design of machine learning applications in radiology. The article shows how radiologists see their professional practice as utilizing a wider range of expert knowledge than many existing studies on machine learning in radiology allow for. The article describes a case study drawn from radiology practice in a major Danish hospital and discusses a widely cited study on machine learning in radiological work. The study connects current understandings of appropriate metrics used by machine learning researchers with professional radiologists' understanding of their diagnostic work. This comparison helps identify gaps in understanding between these two communities and suggests how they might be addressed.
Original language | English |
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Title of host publication | 11th CMI International Conference, 2018 : Prospects and Challenges Towards Developing a Digital Economy within the EU, PCTDDE 2018 |
Editors | Idongesit Williams |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 24 Jan 2019 |
Pages | 56-61 |
Article number | 8624718 |
ISBN (Print) | 978-1-7281-0445-4 |
ISBN (Electronic) | 978-1-7281-0444-7 |
DOIs | |
Publication status | Published - 24 Jan 2019 |
Event | 11TH CMI INTERNATIONAL CONFERENCE - København, Denmark Duration: 29 Nov 2018 → 30 Nov 2018 https://www.conf.cmi.aau.dk/11th+CMI+Conference+2018/ |
Conference
Conference | 11TH CMI INTERNATIONAL CONFERENCE |
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Country/Territory | Denmark |
City | København |
Period | 29/11/2018 → 30/11/2018 |
Internet address |
Keywords
- Diagnostic context
- Machine learning
- Metrics
- Radiology