Medical University of Graz
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics
Jianlong Zhou, Amir H. Gandomi, Fang Chen et al.
2021 · 561 Zit.
Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery
Shane O’Sullivan, Nathalie Nevejans, Colin Allen et al.
2018 · 411 Zit.
Measuring the Quality of Explanations: The System Causability Scale (SCS)
Andreas Holzinger, André Carrington, Heimo Müller
2020 · 378 Zit.
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
Julia K. Winkler, Christine Fink, Ferdinand Toberer et al.
2019 · 359 Zit.
Medical deep learning—A systematic meta-review
Jan Egger, Christina Gsaxner, Antonio Pepe et al.
2022 · 248 Zit.
Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Andreas Holzinger, Matthias Dehmer, Frank Emmert‐Streib et al.
2021 · 208 Zit.
A manifesto on explainability for artificial intelligence in medicine
Carlo Combi, Beatrice Amico, Riccardo Bellazzi et al.
2022 · 148 Zit.
Fairness and Explanation in AI-Informed Decision Making
Alessa Angerschmid, Jianlong Zhou, Kevin Theuermann et al.
2022 · 145 Zit.
Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data
Andreas Holzinger, Benjamin Haibe‐Kains, Igor Jurišica
2019 · 140 Zit.
The explainability paradox: Challenges for xAI in digital pathology
Theodore Evans, Carl Orge Retzlaff, Christian Geißler et al.
2022 · 134 Zit.
The Ten Commandments of Ethical Medical AI
Heimo Müller, Michaela Th. Mayrhofer, Evert-Ben van Veen et al.
2021 · 101 Zit.
The augmented radiologist: artificial intelligence in the practice of radiology
Erich Sorantin, Michael Georg Grasser, Ariane Hemmelmayr et al.
2021 · 96 Zit.
Federated Random Forests can improve local performance of predictive models for various healthcare applications
Anne-Christin Hauschild, Marta Lemanczyk, Julian Matschinske et al.
2022 · 94 Zit.
A pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX) for machine learning
Eszter Nagy, Michael Janisch, Franko Hržić et al.
2022 · 93 Zit.
Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI
Andreas Holzinger, Heimo Müller
2021 · 89 Zit.