Sara Kijewski

Valerie Bürger, Marlie Besouw, Jana Fehr et al.

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.

Jianzhou Yao, Shunchang Liu, Guillaume Drui et al.

Large language models (LLMs) show promise for supporting clinicians in diagnostic communication by generating explanations and guidance for patients. Yet their ability to produce outputs that are both understandable and empathetic remains uncertain. We evaluate two leading LLMs on medical diagnostic scenarios, assessing understandability using readability metrics as a proxy and empathy through LLM-as-a-Judge ratings compared to human evaluations. The results indicate that LLMs adapt explanations to socio-demographic variables and patient conditions. However, they also generate overly complex content and display biased affective empathy, leading to uneven accessibility and support. These patterns underscore the need for systematic calibration to ensure equitable patient communication. The code and data are released: https://github.com/Jeffateth/Biased_Oracle