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Value assessment of artificial intelligence in medical imaging: a scoping review
BACKGROUND: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provide...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620604/ https://www.ncbi.nlm.nih.gov/pubmed/36316665 http://dx.doi.org/10.1186/s12880-022-00918-y |
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author | Fasterholdt, Iben Naghavi-Behzad, Mohammad Rasmussen, Benjamin S. B. Kjølhede, Tue Skjøth, Mette Maria Hildebrandt, Malene Grubbe Kidholm, Kristian |
author_facet | Fasterholdt, Iben Naghavi-Behzad, Mohammad Rasmussen, Benjamin S. B. Kjølhede, Tue Skjøth, Mette Maria Hildebrandt, Malene Grubbe Kidholm, Kristian |
author_sort | Fasterholdt, Iben |
collection | PubMed |
description | BACKGROUND: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS: We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS: Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS: This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-022-00918-y. |
format | Online Article Text |
id | pubmed-9620604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96206042022-11-01 Value assessment of artificial intelligence in medical imaging: a scoping review Fasterholdt, Iben Naghavi-Behzad, Mohammad Rasmussen, Benjamin S. B. Kjølhede, Tue Skjøth, Mette Maria Hildebrandt, Malene Grubbe Kidholm, Kristian BMC Med Imaging Research Article BACKGROUND: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS: We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS: Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS: This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-022-00918-y. BioMed Central 2022-10-31 /pmc/articles/PMC9620604/ /pubmed/36316665 http://dx.doi.org/10.1186/s12880-022-00918-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Fasterholdt, Iben Naghavi-Behzad, Mohammad Rasmussen, Benjamin S. B. Kjølhede, Tue Skjøth, Mette Maria Hildebrandt, Malene Grubbe Kidholm, Kristian Value assessment of artificial intelligence in medical imaging: a scoping review |
title | Value assessment of artificial intelligence in medical imaging: a scoping review |
title_full | Value assessment of artificial intelligence in medical imaging: a scoping review |
title_fullStr | Value assessment of artificial intelligence in medical imaging: a scoping review |
title_full_unstemmed | Value assessment of artificial intelligence in medical imaging: a scoping review |
title_short | Value assessment of artificial intelligence in medical imaging: a scoping review |
title_sort | value assessment of artificial intelligence in medical imaging: a scoping review |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620604/ https://www.ncbi.nlm.nih.gov/pubmed/36316665 http://dx.doi.org/10.1186/s12880-022-00918-y |
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