Andreas Holzinger

What do we need to build explainable AI systems for the medical domain?

2017 · 632 Zit. · arXiv (Cornell University)

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

2021 · 547 Zit. · Electronics

Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery

2018 · 405 Zit. · International Journal of Medical Robotics and Computer Assisted Surgery

Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

2024 · 391 Zit. · Information Fusion

Measuring the Quality of Explanations: The System Causability Scale (SCS)

2020 · 364 Zit. · KI - Künstliche Intelligenz

Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence

2021 · 203 Zit. · Information Fusion

A manifesto on explainability for artificial intelligence in medicine

2022 · 142 Zit. · Artificial Intelligence in Medicine

Fairness and Explanation in AI-Informed Decision Making

2022 · 139 Zit. · Machine Learning and Knowledge Extraction

Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data

2019 · 136 Zit. · European Journal of Nuclear Medicine and Molecular Imaging

The explainability paradox: Challenges for xAI in digital pathology

2022 · 127 Zit. · Future Generation Computer Systems

The Ten Commandments of Ethical Medical AI

2021 · 97 Zit. · Computer

Medical artificial intelligence

2021 · 97 Zit. · Communications of the ACM

The augmented radiologist: artificial intelligence in the practice of radiology

2021 · 92 Zit. · Pediatric Radiology

Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI

2021 · 87 Zit. · Computer

Explainable AI and Multi-Modal Causability in Medicine

2020 · 80 Zit. · i-com