@misc{10481/72110,
year = {2021},
month = {11},
url = {http://hdl.handle.net/10481/72110},
abstract = {Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human
performance at certain tasks. There is no doubt that AI is important to improve human health in many ways
and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond
the lab, in routine environments, we need to do more than to just improve the performance of existing AI
methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and
explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge
as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning
models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that
combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph
causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is
to describe these three areas from a unified view and to motivate how information fusion in a comprehensive
and integrative manner can not only help bring these three areas together, but also have a transformative role
by bridging the gap between research and practical applications in the context of future trustworthy medical
AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future
solutions must not only be ethically responsible, but also legally compliant.},
organization = {Austrian Science Fund (FWF)	P-32554},
organization = {European Union's Horizon 2020 research and innovation program	826078	
965221},
organization = {Spanish Government Juan de la Cierva Incorporacion	IJC2019-039152-I},
organization = {DFF Sapere Aude research leader grant},
organization = {Basque Government	KK-2020/00049},
organization = {consolidated research group MATHMODE	T1294-19},
organization = {Federal Ministry of Education & Research (BMBF)	01IS18025 A	
01IS18037I	
0310L0207C},
organization = {Ontario Research Fund	RDI 34876},
organization = {Natural Sciences Research Council	NSERC 203475},
organization = {Canadian Institutes of Health Research (CIHR)	93579},
organization = {Canada Foundation for Innovation
CGIAR	CFI 29272	
225404	
33536},
organization = {International Business Machines (IBM)},
organization = {Ian Lawson van Toch Fund},
organization = {Schroeder Arthritis Institute via the Toronto General and Western Hospital Foundation},
publisher = {Elsevier},
keywords = {Artificial intelligence},
keywords = {Information fusion},
keywords = {Medical AI},
keywords = {Explainable AI},
keywords = {Robustness},
keywords = {Explainability},
keywords = {Trust},
keywords = {Graph-based machine learning},
keywords = {Neural-symbolic learning and reasoning},
title = {Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence},
doi = {10.1016/j.inffus.2021.10.007},
author = {Holzinger, Andreas and Dehmer, Matthias and Emmert-Streib, Frank and Cucchiara, Rita and Augenstein, Isabelle and Del Ser, Javier and Samek, Wojciech and Jurisica, Igor and Díaz Rodríguez, Natalia Ana},
}