Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Andreas Holzinger1,2, Matthias Dehmer3,4, Frank Emmert-Streib5, Rita Cucchiara6,7, Isabelle Augenstein8, Javier Del Ser9,10, Wojciech Samek11, Igor Jurisica12,13,14,15, Natalia Díaz-Rodríguez16
1Medical University Graz, Austria
2Alberta Machine Intelligence Institute, University of Alberta, Canada
3University of Medical Informatics Tyrol, Austria
4Swiss Distance University of Applied Sciences, Switzerland
5Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
6University of Modena and Reggio Emilia, Modena, Italy
7Artificial Intelligence Research and Innovation Center, Modena, Italy
8Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
9TECNALIA, Basque Research and Technology Alliance (BRTA), Derio, Spain
10University of the Basque Country (UPV/EHU), Bilbao, Spain
11Departments of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Germany
12Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, Canada
13Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, Toronto, Canada
14Departments of Medical Biophysics and Computer Science, University of Toronto, Canada
15Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
16Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI Institute). University of Granada, Spain
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Information Fusion
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