Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. M...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLOS Digital Health |
| Online Access: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000189&type=printable |
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| author | Vasileios Nittas Paola Daniore Constantin Landers Felix Gille Julia Amann Shannon Hubbs Milo Alan Puhan Effy Vayena Alessandro Blasimme |
| author_facet | Vasileios Nittas Paola Daniore Constantin Landers Felix Gille Julia Amann Shannon Hubbs Milo Alan Puhan Effy Vayena Alessandro Blasimme |
| author_sort | Vasileios Nittas |
| collection | DOAJ |
| description | Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner. |
| format | Article |
| id | doaj-art-bf326b1eea674173b596f5a55897561d |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-bf326b1eea674173b596f5a55897561d2025-02-05T05:33:38ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-01-0121e000018910.1371/journal.pdig.0000189Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.Vasileios NittasPaola DanioreConstantin LandersFelix GilleJulia AmannShannon HubbsMilo Alan PuhanEffy VayenaAlessandro BlasimmeMachine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000189&type=printable |
| spellingShingle | Vasileios Nittas Paola Daniore Constantin Landers Felix Gille Julia Amann Shannon Hubbs Milo Alan Puhan Effy Vayena Alessandro Blasimme Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS Digital Health |
| title | Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. |
| title_full | Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. |
| title_fullStr | Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. |
| title_full_unstemmed | Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. |
| title_short | Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. |
| title_sort | beyond high hopes a scoping review of the 2019 2021 scientific discourse on machine learning in medical imaging |
| url | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000189&type=printable |
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