Clinical translation of computational brain models: understanding the salience of trust in clinician-researcher relationships.

Computational brain models use machine learning algorithms and statistical models to harness big data for delivering disease-specific diagnosis or prognosis for individuals. They are intended to support clinical decision making and are widely available. However, their translation into clinical practice remains weak despite efforts to improve implementation such as through training clinicians and clinical staff in their use and benefits. In this paper, we argue that it is necessary to go beyond existing implementation efforts to understand and meaningfully integrate the clinician's perspective and tacit knowledge for translating computational brain models in neurological practice. The empirical research draws on our collective seven-year engagement with the Human Brain Project as researchers of its 'Ethics and Society' subproject and includes analysis of published and grey literature, participant observation at workshops and conferences, and interviews with data scientists, neuroscientists, and neurologists in the UK and Europe developing computational tools for neurology. Our findings show that building trust in the relationships between clinicians and researchers (modelers, data scientists) through meaningful upstream collaboration, greater model transparency and integration of tacit knowledge play a salient role in translation processes with meaningful benefit for patients.