Marco S. Nobile

A Theoretical Framework for AI Models Explainability with Application in Biomedicine

2023 · 19 Zit.

Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making

2025 · 3 Zit. · BMC Medical Research Methodology

Trustworthy Artificial Intelligence in Medical Applications: A Mini Survey

2023 · 2 Zit.

FanFAIR: sensitive data sets semi-automatic fairness assessment

2025 · 1 Zit. · BMC Medical Informatics and Decision Making

Trustworthy Machine Learning Predictions to Support Clinical Research and Decisions

2023 · 0 Zit.

Assessing the Value of Explainable Artificial Intelligence for Magnetic Resonance Imaging

2025 · 0 Zit. · Communications in computer and information science

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

2026 · 0 Zit. · Research at the University of Copenhagen (University of Copenhagen)

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

2026 · 0 Zit. · arXiv (Cornell University)

Assessing Cardiac Functionality by Means of Interpretable AI and Myocardial Strain

2025 · 0 Zit.

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

2026 · 0 Zit. · arXiv (Cornell University)