AI-enabled passive digital phenotyping of experiential negative symptoms in schizophrenia-spectrum disorders: a narrative review on the path from sensing to clinical endpoints.

Negative symptoms, particularly avolition and asociality, rank among the most disabling and treatment-resistant features of schizophrenia-spectrum disorders. Clinician-rated scales such as the Clinical Assessment Interview for Negative Symptoms and the Brief Negative Symptom Scale have strengthened construct definition. However, they depend on infrequent interviews and retrospective recall, missing the moment-to-moment behavioral patterns that define these symptoms in everyday life. Passive digital phenotyping, through smartphones and wearable sensors, can continuously capture mobility, physical activity, sleep, social rhythms, and speech acoustics under real-world conditions. Whether artificial intelligence can convert these noisy, incomplete, and context-dependent data streams into clinically valid digital endpoints remains the central unresolved question. This narrative review synthesizes evidence on AI-enabled passive digital phenotyping of experiential negative symptoms in schizophrenia-spectrum disorders. We propose a construct-to-endpoint pipeline that links construct definition, multimodal representation learning, and fit-for-purpose clinical validation to interpretable digital markers and just-in-time adaptive interventions. Current evidence reveals consistent but modest associations between passive sensing modalities and negative symptoms. However, single-modality correlations do not constitute valid endpoints. A qualified digital endpoint must demonstrate analytical validity, clinical validity, longitudinal sensitivity, external transportability, and patient meaningfulness. Key challenges span data-level issues such as resolution mismatch and device heterogeneity, inferential risks including environmental confounding and algorithmic bias, and ethical-regulatory concerns involving privacy and model opacity. The field should now prioritize construct-anchored, context-aware, and interpretable digital endpoints that can serve as outcome measures in negative-symptom trials and support personalized longitudinal monitoring.