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Verfasst von:Ciobanu-Caraus, Olga [VerfasserIn]   i
 Aicher, Anatol [VerfasserIn]   i
 Kernbach, Julius [VerfasserIn]   i
 Regli, Luca [VerfasserIn]   i
 Serra, Carlo [VerfasserIn]   i
 Staartjes, Victor E. [VerfasserIn]   i
Titel:A critical moment in machine learning in medicine
Titelzusatz:on reproducible and interpretable learning
Verf.angabe:Olga Ciobanu-Caraus, Anatol Aicher, Julius M. Kernbach, Luca Regli, Carlo Serra, Victor E. Staartjes
E-Jahr:2024
Jahr:16 January 2024
Umfang:7 S.
Fussnoten:Gesehen am 06.03.2024
Titel Quelle:Enthalten in: Acta neurochirurgica
Ort Quelle:Wien [u.a.] : Springer, 1950
Jahr Quelle:2024
Band/Heft Quelle:166(2024), 1, Artikel-ID 14, Seite 1-7
ISSN Quelle:0942-0940
Abstract:Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients’ health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the “black box”. To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.
DOI:doi:10.1007/s00701-024-05892-8
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1007/s00701-024-05892-8
 kostenfrei: Volltext: https://link.springer.com/article/10.1007/s00701-024-05892-8
 DOI: https://doi.org/10.1007/s00701-024-05892-8
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Interpretability
 Machine learning
 Methodology
 Reproducibility
K10plus-PPN:1882661591
Verknüpfungen:→ Zeitschrift

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