Julia Amann

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

2020 · 1.635 Zit. · BMC Medical Informatics and Decision Making

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

2022 · 190 Zit. · PLOS Digital Health

Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

2023 · 85 Zit. · PLoS ONE

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

2021 · 64 Zit. · Frontiers in Human Dynamics

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

2021 · 51 Zit. · Frontiers in Human Dynamics

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

2022 · 35 Zit. · IEEE Transactions on Technology and Society

Lessons Learned from Assessing Trustworthy AI in Practice

2023 · 24 Zit. · Digital Society

Machine Learning in Stroke Medicine: Opportunities and Challenges for Risk Prediction and Prevention

2021 · 17 Zit. · Advances in neuroethics

The unmet promise of trustworthy AI in healthcare: why we fail at clinical translation

2024 · 17 Zit. · Frontiers in Digital Health

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

2023 · 16 Zit. · PLOS Digital Health

How to Assess Trustworthy AI in Practice

2022 · 10 Zit. · arXiv (Cornell University)

The fundamentals of AI ethics in medical imaging

2025 · 1 Zit. · Elsevier eBooks

Operationalizing AI ethics in medicine—a co-creation workshop study

2025 · 1 Zit. · BMC Medical Ethics