Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

How Meta-research Can Pave the Road Towards Trustworthy AI In Healthcare: Catalogue of Ideas and Roadmap for Future Research

Title: How Meta-research Can Pave the Road Towards Trustworthy AI In Healthcare: Catalogue of Ideas and Roadmap for Future Research
Authors: Bürger, Valerie; Besouw, Marlie; Fehr, Jana; Minocher, Riana; Moorhead, Emma; Velarde, Isabel; Agha-Mir-Salim, Louis; Amann, Julia; Bannach-Brown, Alexandra; Blumenthal, David B.; Hair, Kaitlyn; Heinrichs, Bert; Herrmann, Moritz; Hofvenschiöld, Elizabeth; Holm, Sune; de Hond, Anne A. H.; Kijewski, Sara; McLennan, Stuart; Minssen, Timo; Nobile, Marco S.; Pfeifer, Nico; Rohmann, Jessica L.; Ross-Hellauer, Tony; Slavkovik, Marija; Tafur, Karin; Viganò, Eleonora; Westerlund, Magnus; Weissgerber, Tracey; Madai, Vince I.
Publication Year: 2026
Collection: ArXiv.org (Cornell University Library)
Subject Terms: Computers and Society; Artificial Intelligence
Description: Meta-research and Trustworthy AI (TAI) share common goals, namely improving evidence, robustness, and transparency, yet there is very little interplay between the two fields. To investigate the potential benefits of closer collaboration between the domains of TAI in healthcare and meta-research, we convened an interdisciplinary workshop funded by the Volkswagen Foundation in February 2025. The workshop aimed to collaboratively examine key tensions in translating AI ethics principles into practice and to identify potential solutions informed by meta-research approaches. A Design Thinking-informed co-creation approach was followed by an inductive descriptive analysis of the outputs. Our results demonstrate how meta-research can offer concrete contributions to address pressing challenges of TAI in healthcare. These challenges include achieving robustness, reproducibility, and replicability; late-stage development and the integration of AI into clinical practice; the selection of appropriate evaluation metrics; specific AI-related challenges in preclinical and biomedical research; gaps of transparency in medical AI, as well as the need for improved conceptual clarity and AI literacy among stakeholders. Finally, we offer a catalog of ideas and roadmap for future research to inform scholars in both fields on existing interconnections and serve as a foundation for guiding future interdisciplinary efforts.
Document Type: text
Language: unknown
Relation: http://arxiv.org/abs/2603.13286
Availability: http://arxiv.org/abs/2603.13286
Accession Number: edsbas.985D13E0
Database: BASE