Paolo Corsico1; 1School of Law, the University of Manchester
Identification of individuals at high risk of psychosis has usually been performed by means of a clinical interview, such as the Structured Interview for Prodromal Syndromes (SIPS), and the Comprehensive Assessment of At-Risk Mental State (CAARMS). Yet, the relatively low transitions rates of individuals identified as being at risk of psychosis have sparked great attention for neuroscientific measures of psychosis risk. Particularly, neuroimaging and machine learning could soon support the identification of individuals at high risk of psychosis, and ameliorate diagnosis and prediction of psychosis transition in high-risk populations. In this presentation, I address the ethical issues that arise from the attempt to identify youth at risk of psychosis via neuroimaging and machine learning methods. First, I outline the ethical issues that arise from involving young (help-seeking) individuals in neuroimaging research. Along with issues of research and data governance, I shall focus on the lack of immediate clinical utility, and on the challenges of communicating psychosis risk in a research setting. Second, I outline the ethical issues that may derive from the translation of neuroimaging and machine learning as predictive and diagnostic tools in the clinical setting. Here, I shall focus on the risk to reinforce neuro-essentialist thinking in young individuals. Overall, I argue that the clinical benefits gained by translating novel predictive tools in the clinical setting ought to be weighed against potential risks for young populations.
