Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop

TL;DR

This paper reports on an interdisciplinary workshop exploring how meta-research can enhance Trustworthy AI in healthcare, identifying key challenges and proposing a roadmap for future collaborative efforts.

Contribution

It demonstrates how meta-research can address complex challenges in implementing Trustworthy AI in healthcare through a collaborative, design-thinking approach.

Findings

Meta-research offers concrete solutions to TAI challenges in healthcare.

Identified key issues like transparency, robustness, and validation in TAI.

Presented a roadmap for future interdisciplinary research efforts.

Abstract

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.