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eXplainable Artificial Intelligence in Healthcare Management A new Online Interdisciplinary Master’s Program at the Intersection of AI and Health Care

What will you learn? xAIM in short: The Project The eXplainable Artificial Intelligence in Healthcare Management Masters is developed within an xAIM project supported by a Connecting Europe Facility in Telecom (project INEA/CEF/ICT/A2020/2276680). Our...

Ethical Implication of AI: Assessing Trustworthy AI in Practice: Lecture Notes (Open Access)

Target Students Master students and PhD students from interdisciplinary background. (e.g. Computer Science, Data Science, Machine Learning, Law, Medicine, Social Science, Ethics, Public Policy, etc.).   Course Description Applications based on Machine Learning and/or Deep Learning...

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

Roberto V. Zicari, Sheraz Ahmed, Julia Amann, Stephan Alexander Braun, John Brodersen, Frédérick Bruneault, James Brusseau, Erik Campano , Megan Coffee, Andreas Dengel, Boris Düdder, Alessio Gallucci, Thomas Krendl Gilbert, Philippe Gottfrois, Emmanuel Goffi, Christoffer...

Getting Ready for the EU AI Act in Healthcare. A call for Sustainable AI Development and Deployment

John Brandt Brodersen (1 and 2), Ilaria Amelia Caggiano (3), Pedro Kringen (4), Vince Istvan Madai (5), Walter Osika (6 and 7), Giovanni Sartor (8 and 9), Ellen Svensson (6 and 10), Magnus Westerlund (4), Roberto V. Zicari (11) Assessments of trustworthiness have become a cornerstone of responsible AI development. Especially...

How to Assess Trustworthy AI in Practice.

Roberto V. Zicari, Julia Amann, Frédérick Bruneault, Megan Coffee, Boris Düdder, Eleanore Hickman, Alessio Gallucci, Thomas Krendl Gilbert, Thilo Hagendorff, Irmhild van Halem, Elisabeth Hildt, Georgios Kararigas, Pedro Kringen, Vince I. Madai, Emilie Wiinblad Mathez, Jesmin Jahan Tithi, Dennis Vetter, Magnus Westerlund, Renee WurthOn behalf of the Z-Inspection® initiative (2022) AbstractThis report...

Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients.

Himanshi Allahabadi, Julia Amann, Isabelle Balot , Andrea Beretta, Charles Binkley , Jonas Bozenhard , Frédérick Bruneault, James Brusseau , Sema Candemir, Luca Alessandro Cappellini , Subrata Chakraborty , Senior Member, IEEE, Nicoleta Cherciu, Christina...

Lessons Learned from Assessing Trustworthy AI in Practice.

Dennis Vetter, Julia Amann, Frederick Bruneault, Megan Coffee, Boris Düdder, Alessio Gallucci, Thomas Krendl Gilbert, Thilo Hagendorff, Irmhild van Halem, Dr Eleanore Hickman, Elisabeth Hildt, Sune Holm, George Kararigas,Pedro Kringen, Vince Madai , Emilie Wiinblad Mathez, Jesmin Jahan Tithi, Ph.D , Magnus Westerlund, Renee Wurth, PhD, Roberto V. Zicari & Z-Inspection® initiative (2022) Abstract Building artificial...

Validation of a Trustworthy AI-based Clinical Decision Support System for Improving Patient Outcome in Acute Stroke Treatment

DELIVERABLE: D1.2 – Z-inspection® process result report, 31.10.2023 Cathrine Kieu Trang Bui (CUB) Vince Madai Istvan (CUB) Gabriele Pluktaite (CUB) Steven Hicks (SIM), Luis Miguel Lopez Ramos (SIM) with contributions from Elizabeth Hofvenschioeld, Adam Hilbert, Susanne...

Lessons Learned in Performing a Trustworthy AI and Fundamental Rights Assessment.

Marjolein Boonstra, Frédérick Bruneault, Subrata Chakraborty, Tjitske Faber, Alessio Gallucci, Eleanore Hickman, Gerard Kema, Heejin Kim, Jaap Kooiker, Elisabeth Hildt, Annegret Lamadé, Emilie Wiinblad Mathez, Florian Möslein, Genien Pathuis, Giovanni Sartor, Marijke Steege, Alice Stocco, Willy Tadema, Jarno Tuimala, Isabel van Vledder, Dennis Vetter, Jana Vetter, Magnus Westerlund, Roberto V. Zicari This report shares the experiences, results...