The role of trustworthy and reliable AI for multiple sclerosis
This paper investigates the importance of Trustworthy Machine Learning (ML) in the context of Multiple Sclerosis (MS) research and care. Due to the complex and individual nature of MS, the need for reliable and trustworthy ML models is essential. In this paper, key aspects of trustworthy ML, such as...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1507159/full |
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| author | Lorin Werthen-Brabants Tom Dhaene Dirk Deschrijver |
| author_facet | Lorin Werthen-Brabants Tom Dhaene Dirk Deschrijver |
| author_sort | Lorin Werthen-Brabants |
| collection | DOAJ |
| description | This paper investigates the importance of Trustworthy Machine Learning (ML) in the context of Multiple Sclerosis (MS) research and care. Due to the complex and individual nature of MS, the need for reliable and trustworthy ML models is essential. In this paper, key aspects of trustworthy ML, such as out-of-distribution generalization, explainability, uncertainty quantification and calibration are explored, highlighting their significance for healthcare applications. Challenges in integrating these ML tools into clinical workflows are addressed, discussing the difficulties in interpreting AI outputs, data diversity, and the need for comprehensive, quality data. It calls for collaborative efforts among researchers, clinicians, and policymakers to develop ML solutions that are technically sound, clinically relevant, and patient-centric. |
| format | Article |
| id | doaj-art-bd951291bd654dc097ce29ccca33c0ea |
| institution | Kabale University |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-bd951291bd654dc097ce29ccca33c0ea2025-08-20T03:39:57ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-03-01710.3389/fdgth.2025.15071591507159The role of trustworthy and reliable AI for multiple sclerosisLorin Werthen-BrabantsTom DhaeneDirk DeschrijverThis paper investigates the importance of Trustworthy Machine Learning (ML) in the context of Multiple Sclerosis (MS) research and care. Due to the complex and individual nature of MS, the need for reliable and trustworthy ML models is essential. In this paper, key aspects of trustworthy ML, such as out-of-distribution generalization, explainability, uncertainty quantification and calibration are explored, highlighting their significance for healthcare applications. Challenges in integrating these ML tools into clinical workflows are addressed, discussing the difficulties in interpreting AI outputs, data diversity, and the need for comprehensive, quality data. It calls for collaborative efforts among researchers, clinicians, and policymakers to develop ML solutions that are technically sound, clinically relevant, and patient-centric.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1507159/fullartificial intelligencemultiple sclerosistrustworthy AIdeep learninguncertainty quantification |
| spellingShingle | Lorin Werthen-Brabants Tom Dhaene Dirk Deschrijver The role of trustworthy and reliable AI for multiple sclerosis Frontiers in Digital Health artificial intelligence multiple sclerosis trustworthy AI deep learning uncertainty quantification |
| title | The role of trustworthy and reliable AI for multiple sclerosis |
| title_full | The role of trustworthy and reliable AI for multiple sclerosis |
| title_fullStr | The role of trustworthy and reliable AI for multiple sclerosis |
| title_full_unstemmed | The role of trustworthy and reliable AI for multiple sclerosis |
| title_short | The role of trustworthy and reliable AI for multiple sclerosis |
| title_sort | role of trustworthy and reliable ai for multiple sclerosis |
| topic | artificial intelligence multiple sclerosis trustworthy AI deep learning uncertainty quantification |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1507159/full |
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