ETH Zurich
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
Julia Amann, Alessandro Blasimme, Effy Vayena et al.
2020 · 1.634 Zit.
Machine learning in medicine: Addressing ethical challenges
Effy Vayena, Alessandro Blasimme, I. Glenn Cohen
2018 · 745 Zit.
Advances, challenges and opportunities in creating data for trustworthy AI
Weixin Liang, Girmaw Abebe Tadesse, Daniel E. Ho et al.
2022 · 468 Zit.
Automated Gleason grading of prostate cancer tissue microarrays via deep learning
Eirini Arvaniti, Kim S. Fricker, Michaël Moret et al.
2018 · 420 Zit.
Considerations for ethics review of big data health research: A scoping review
Marcello Ienca, Agata Ferretti, Samia Hurst et al.
2018 · 250 Zit.
Mitigating bias in machine learning for medicine
Kerstin Noëlle Vokinger, Stefan Feuerriegel, Aaron S. Kesselheim
2021 · 234 Zit.
In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions
Andrea Ferrario, Michele Loi, Eleonora Viganò
2019 · 206 Zit.
To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
Julia Amann, Dennis Vetter, Stig Nikolaj Fasmer Blomberg et al.
2022 · 190 Zit.
The Challenges for Regulating Medical Use of ChatGPT and Other Large Language Models
Timo Minssen, Effy Vayena, I. Glenn Cohen
2023 · 160 Zit.
What we talk about when we talk about trust: Theory of trust for AI in healthcare
Felix Gille, Anna Jobin, Marcello Ienca
2020 · 155 Zit.
A conversation with ChatGPT on the role of computational systems biology in stem cell research
Patrick Cahan, Barbara Treutlein
2023 · 132 Zit.
How Explainability Contributes to Trust in AI
Andrea Ferrario, Michele Loi
2022 · 117 Zit.
From Scarcity to Abundance: Scholars and Scholarship in an Age of Generative Artificial Intelligence
Matthew Grimes, Georg von Krogh, Stefan Feuerriegel et al.
2023 · 115 Zit.
Implications of artificial intelligence for medical education
Vanessa Rampton, Michael Mittelman, Jörg Goldhahn
2020 · 111 Zit.
Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals
Nadine Bienefeld, J. M. Boss, Rahel Lüthy et al.
2023 · 110 Zit.