Advancing multimodal neuroimaging: Explainable and responsible AI for early dementia detection

Artificial intelligence (AI) has not seen the clinical uptake that might be expected from a technology that has received so much attention and investment. Coupled to neuroimaging, it is conceivable that AI algorithms can provide better performance in diagnosis and prognosis as well as optimize treatments in a precision medicine regimen, all of which is focused on improving both the patient experience and clinical outcomes. But in practice, little impact has been seen. Why might this be the case? This overview focuses on the possible reasons. First, technical concerns: the way in which AI algorithms are developed and validated in the research setting does not adequately prepare them for deployment in clinics and hospitals. Second, the importance of asking clinical questions with AI algorithms that have meaning and value is often underplayed or not considered. The outputs of AI algorithms mostly, but not always, also need to explain how decisions have been made. Thirdly, operational and ethical considerations loom over the integration of AI algorithms into electronic health record systems, clinical pathways, and legal frameworks. Above all these considerations is the motivation for deployment and particularly whether it is primarily for patient benefit or service economics.