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.

翻译：元研究与可信赖人工智能（TAI）具有共同目标，即改进证据、稳健性和透明度，但两个领域之间的互动非常有限。为探究医疗领域的TAI与元研究之间更紧密合作的潜在益处，我们于2025年2月组织了一场由大众汽车基金会资助的跨学科研讨会。该研讨会旨在通过协作方式审视将AI伦理原则转化为实践的关键挑战，并识别由元研究方法启发的潜在解决方案。我们采用基于设计思维的共创方法，随后对产出进行了归纳性描述分析。研究结果表明，元研究能够为应对医疗领域TAI的紧迫挑战做出具体贡献。这些挑战包括：TAI伦理要求与原则的动态性和复杂性、TAI的通用术语与理解、确保稳健性、可重复性和可再现性、选择恰当的评估指标、缺乏透明度、推进临床前生物医学研究，以及在实际临床环境中的验证。我们提出了一份研究构想目录和未来研究路线图，综合了现有关联性，明确了具体后续步骤与未解决的研究空白，从而为未来跨学科努力奠定基础。