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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.13286 |
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| _version_ | 1866910193847107584 |
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| author | 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. |
| author_facet | 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. |
| contents | 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 challenges 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 the dynamic and complex nature of TAI ethical requirements and principles, common terminology and understanding of TAI, ensuring robustness, replicability, and reproducibility, choosing adequate evaluation metrics, lack of transparency, advancing preclinical biomedical research, and validation in real-world clinical environments. We present a catalog of ideas and a roadmap for future research, which synthesize existing interconnections and identify concrete next steps and open research gaps, thereby serving as a foundation for future interdisciplinary efforts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13286 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop 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. Computers and Society Artificial Intelligence 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 challenges 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 the dynamic and complex nature of TAI ethical requirements and principles, common terminology and understanding of TAI, ensuring robustness, replicability, and reproducibility, choosing adequate evaluation metrics, lack of transparency, advancing preclinical biomedical research, and validation in real-world clinical environments. We present a catalog of ideas and a roadmap for future research, which synthesize existing interconnections and identify concrete next steps and open research gaps, thereby serving as a foundation for future interdisciplinary efforts. |
| title | Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2603.13286 |