116 citations · 179 across the 11 of their papers we have counts for
11 papers
Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop
Valerie Bürger, Marlie Besouw, Jana Fehr +26
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 in…
Getting Ready for the EU AI Act in Healthcare. A call for Sustainable AI Development and Deployment
John Brandt Brodersen, Ilaria Amelia Caggiano, Pedro Kringen +6
Assessments of trustworthiness have become a cornerstone of responsible AI development. Especially in high-stakes fields like healthcare, aligning technical, evidence-based, and et…
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi +6
The joint implementation of federated learning (FL) and explainable artificial intelligence (XAI) could allow training models from distributed data and explaining their inner worki…
Explainable AI: Definition and attributes of a good explanation for health AI
Evangelia Kyrimi, Scott McLachlan, Jared M Wohlgemut +4
Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity…
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Patrick Rockenschaub, Adam Hilbert, Tabea Kossen +3
Deep learning (DL) can aid doctors in detecting worsening patient states early, affording them time to react and prevent bad outcomes. While DL-based early warning models usually w…
How to Assess Trustworthy AI in Practice
Roberto V. Zicari, Julia Amann, Frédérick Bruneault +17
This report is a methodological reflection on Z-Inspection. Z-Inspection is a holistic process used to evaluate the trustworthiness of A…
Artificial Intelligence in Healthcare: Lost In Translation?
Vince I. Madai, David C. Higgins
Artificial intelligence (AI) in healthcare is a potentially revolutionary tool to achieve improved healthcare outcomes while reducing overall health costs. While many exploratory r…
The social dilemma in artificial intelligence development and why we have to solve it
Inga Strümke, Marija Slavkovik, Vince I. Madai
While the demand for ethical artificial intelligence (AI) systems increases, the number of unethical uses of AI accelerates, even though there is no shortage of ethical guidelines.…
On The Usage Of Average Hausdorff Distance For Segmentation Performance Assessment: Hidden Bias When Used For Ranking
Orhun Utku Aydin, Abdel Aziz Taha, Adam Hilbert +5
Average Hausdorff Distance (AVD) is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, AVD is used to compare ground…
Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks
Tabea Kossen, Pooja Subramaniam, Vince I. Madai +9
Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis. This is a big challenge, especially for neuroimaging…
From Bit To Bedside: A Practical Framework For Artificial Intelligence Product Development In Healthcare
David Higgins, Vince I. Madai
Artificial Intelligence (AI) in healthcare holds great potential to expand access to high-quality medical care, whilst reducing overall systemic costs. Despite hitting the headline…