ObjectiveRecent advances in generative artificial intelligence (AI) and dimensional approaches in psychiatry offer scalable scoring of psychopathology, yet biological validation remains challenging. This study aimed to compare AI and human performances in scoring dimensional psychopathology and its relationship with inflammatory brain markers in psychotic disorders.MethodsIn a cross-sectional, real-world, prospective study, we generated research domain criteria (RDoC) profiles using a large-language model and human ratings from admission notes of 127 consecutively selected patients with psychotic disorders. Magnetic resonance imaging (MRI) diffusion-based restricted fraction (RF) values were extracted from the amygdala, hippocampus, and neocortex as a proxy of inflammation. We assessed the agreement between AI- and human-derived scores and their predictive value for regional RF.ResultsAI and human RDoC ratings showed moderate-to-high agreement (intraclass correlation coefficients: 0.65-0.81). AI-derived, but not human-derived, negative and positive valence RDoC scores predicted amygdala and neocortical inflammation, while social and regulatory/arousal scores predicted hippocampal RF. A significant association was found between neocortical RF and regulatory/arousal scores in the AI assessment. Both AI- and human-derived cognitive scores predicted cortical RF. When the regression analyses were corrected for multiple comparisons, only the AI-derived associations remained significant: the amygdala for negative valence and the cortex for regulatory/arousal scores.ConclusionsThese results suggest a significant correspondence between AI and human RDoC ratings. AI-based dimensional phenotyping may reflect underlying neuroinflammatory processes, offering a biologically anchored tool for precision psychiatry.
Kéri et al. (Thu,) studied this question.