Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The WMA Declaration of Helsinki – Revision 2024: A synopsis and perspectives for professionals in the domain of medical informatics, biometry, and epidemiology
0
Zitationen
5
Autoren
2026
Jahr
Abstract
Background: Sixty years after its introduction in 1964, the Declaration of Helsinki (DoH) was once more revised in 2024. In this article, we provide a brief historical overview followed by an introduction to the DoH to the interdisciplinary and interprofessional domains of medical informatics, biometry, and epidemiology. Methods: We performed a critical synopsis of the 2013 and 2024 versions of the DoH, discussing the adequacy of the updates to existing and emerging challenges of medical research in the domain of medical informatics, biometry, and epidemiology. Results: Major updates of the new version are the extension of the scope of addressees to all researchers related to medical research involving human participants. Further, it adapts terminological changes from the current discourse in applied ethics and strengthens demands for sustainability and equity. The responsibility of researchers is extended to encompass society as a whole, beyond the study population. It does not explicitly include principles on artificial intelligence (AI) in medical research. However, its principles are applicable to AI and address many AI relevant points. Discussion: In comparison to the 2013 version, the 2024 version of the DoH shows significant improvements concerning the role of participants, sustainability, and justice. To implement the extended scope of addressees, it poses new challenges to policy making in professional laws or associations and societies to adapt the DoH in their rules of conduct. Concerns on AI in medical research seem to be largely covered by existing articles of the DoH. Still, having explicit ethical guidelines for using AI in medical research could be helpful in working out specific recommendations for dealing with the risks and possibilities of AI.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.260 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.116 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.493 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.438 Zit.