TY - JOUR
T1 - Clinical translation of computational brain models: understanding the salience of trust in clinician-researcher relationships
AU - Datta Burton, Saheli
AU - Mahfoud, Tara
AU - Aicardi, Christine
AU - Rose, Nikolas
N1 - Funding Information:
The authors acknowledge support from the European Union's Horizon 2020 Research and Innovation Framework Programme funding for the Human Brain Project (Special Grant Agreement 2 number 785907); and the United Kingdom Engineering and Physical Sciences Research Council grant for the PETRAS-2 project (grant number EP/S03562/1). We would like to convey our special thanks to Dr Edison Bicudo (University of Sussex, UK), Professor Alex Faulkner (University of Sussex, UK), and to all who participated in this research. The authors acknowledge support from the European Union?s Horizon 2020 Research and Innovation Programme funding for the Human Brain Project (Special Grant Agreement 2 number 785907).
Publisher Copyright:
© 2020 Institute of Materials, Minerals and Mining Published by Taylor & Francis on behalf of the Institute.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/3/7
Y1 - 2021/3/7
N2 - Computational brain models use machine learning, algorithms and statistical models to harness big data for delivering disease-specific diagnosis or prognosis for individuals. While intended to support clinical decision-making, their translation into clinical practice remains challenging despite efforts to improve implementation through training clinicians and clinical staff in their use and benefits. Drawing on the specific case of neurology, we argue that existing implementation efforts are insufficient for the responsible translation of computational models. Our research based on a collective seven-year engagement with the Human Brain Project, participant observation at workshops and conferences, and expert interviews, suggests that relationships of trust between clinicians and researchers (modellers, data scientists) are essential to the meaningful translation of computational models. In particular, efforts to increase model transparency, strengthen upstream collaboration, and integrate clinicians' perspectives and tacit knowledge have the potential to reinforce trust building and increase translation of technologies that are beneficial to patients.
AB - Computational brain models use machine learning, algorithms and statistical models to harness big data for delivering disease-specific diagnosis or prognosis for individuals. While intended to support clinical decision-making, their translation into clinical practice remains challenging despite efforts to improve implementation through training clinicians and clinical staff in their use and benefits. Drawing on the specific case of neurology, we argue that existing implementation efforts are insufficient for the responsible translation of computational models. Our research based on a collective seven-year engagement with the Human Brain Project, participant observation at workshops and conferences, and expert interviews, suggests that relationships of trust between clinicians and researchers (modellers, data scientists) are essential to the meaningful translation of computational models. In particular, efforts to increase model transparency, strengthen upstream collaboration, and integrate clinicians' perspectives and tacit knowledge have the potential to reinforce trust building and increase translation of technologies that are beneficial to patients.
UR - http://www.scopus.com/inward/record.url?scp=85102206569&partnerID=8YFLogxK
U2 - 10.1080/03080188.2020.1840223
DO - 10.1080/03080188.2020.1840223
M3 - Article
SN - 0308-0188
VL - 46
SP - 138
EP - 157
JO - INTERDISCIPLINARY SCIENCE REVIEWS
JF - INTERDISCIPLINARY SCIENCE REVIEWS
IS - 1-2
ER -
