Dementia in general and Alzheimer's disease (AD) in particular impose an increasing burden on aging societies. The Dutch LeARN project is presently developing early diagnostic technologies for AD, including PET, MRI and CSF tests. These molecular diagnostics promise to advance AD diagnosis and improve its reliability, and thus may reduce uncertainty in people suffering from cognitive dysfunctions. However, they raise new uncertainties as well, for example about usefulness in clinical practice, the response of potential users, the potential transformation of care practices, and the desirability of early diagnostics for AD, especially when effective therapies are lacking. Responsible innovation in this context means that such uncertainties and the concomitant stakeholders are systematically addressed at an early stage of technology development. The proposed research project aims to contribute to a morally desirable and socially robust diagnostic practice for AD, by (i) identifying scientific and clinical uncertainties in technology development, (ii) analyzing the social and cultural as well as the (iii) moral implications of existing and alternative ways to deal with them, and (iv) to design strategies for responsible uncertainty reduction in innovation of AD diagnostics. Since uncertainties, stakeholders and strategies are often interconnected, the research will be interdisciplinary. In addition, engagement of future users and stakeholders in the innovation process will be an important tool to anticipate and moderate the potential impact of the proposed innovations. The ultimate goal is to design a model for responsible innovation of diagnostic technology in a translational setting.
Research team
- dr. E. Moors, Dep. of Innovation Studies, University of Utrecht
- dr. H. van Lente, Dep. of Innovation Studies, University of Utrecht
- prof. dr. M. van Buchem, Dep. of Radiology, Leiden University Medical Center (LeARN)
- dr. B. van Berckel, Dep. of Nuclear Medicine and PET Research, Vrije Universiteit Medical Centre (LeARN)