The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can
aid in objectively diagnosing patients and providing individualized treatment …

The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …

The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …

Deep learning can provide rapid brain age estimation based on brain magnetic resonance
imaging (MRI). However, most studies use one neural network to extract the global …

Background The clinical high‐risk state for psychosis (CHR‐P) paradigm has facilitated the
implementation of psychosis prevention into clinical practice; however, advancements in …

Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …

Scientific progress in understanding human disease can be measured by the effectiveness
of its treatment. Antipsychotic drugs have been proven to alleviate acute psychotic symptoms …

Brain age prediction using machine‐learning techniques has recently attracted growing
attention, as it has the potential to serve as a biomarker for characterizing the typical brain …

Background: Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly
being integrated into health care. As studies on attitudes toward AI have primarily focused …

Brain age prediction based on imaging data and machine learning (ML) methods has great
potential to provide insights into the development of cognition and mental disorders. Though …