Breakthroughs in artificial intelligence (AI) have transformed diagnostics in cognitive health, particularly within the realms of neuroimaging. New technologies, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have made it possible to detect previously subclinical changes in patients long before the associated changes become symptomatic, as well as synthesise and correct medical images. The diagnostic tools provide an unparalleled accuracy of anomalies at the tissue level, which permits timely and personalised interventions using multimodal data, including genetics, imaging, and behavioural data. AI improves diagnostic precision, thus enabling cross-population generalisability, which addresses dataset imbalance through synthesis, and augments clinician trust through explainable interfaces. However, the implementation of such systems is not without challenges, such as a disparity in diagnostic criteria (e.g., DSM-5 vs. ICD-10/11), deficits in longitudinal samples, and structural biases within the training data. Technical innovations need to be matched with evolution in ethics, law, and regulation to safeguard patients’ data and autonomy while ensuring fairness in the access and distribution of care. Federated systems and learning ‘human-in-the-loop’ models may mitigate this problem by enabling shared-ownership model construction across multiple health systems, while maintaining clinician supervision of model and system deployment. This chapter brings together the clinical, technical, and ethical considerations of AI-assisted cognitive diagnosis. It underscores the importance of standardization regarding differing diagnostic processes in different locations, interprofessional collaboration, and continuous cross-validation for the reproducible, transparent, and accountable incorporation of AI. Ultimately, the fusion of medicine and technology offers the prospect of a paradigm shift in cognitive healthcare, emphasizing the shift toward proactive and individualized approaches. This fusion could transform the diagnosis, evaluation, and treatment processes of cognitive disorders.