Privacy-Preserving Closed-Loop Edge AI for Continuous Behavioral Biomarker Extraction and Psychiatric Diagnostic Inference

Abstract

Publication status. This manuscript is a critical narrative review and translational framework, not a registered systematic review or meta-analysis. It synthesizes targeted peer-reviewed biomedical and engineering literature, methodological guidance, and the supplied PDAD dossier. The evidence is used to separate established findings from proposed architecture and research hypotheses. Abstract Artificial intelligence (AI), digital phenotyping, passive sensing, and edge computing are increasingly being explored to extend psychiatric assessment beyond intermittent clinical encounters. This review evaluates the evidence relevant to a privacy-preserving, closed-loop architecture in which environmental and wearable signals are processed locally, converted into abstract behavioral biomarkers, and used to support longitudinal diagnostic reasoning. The literature indicates that digital phenotyping can capture clinically relevant changes in sleep, activity, mobility, communication, speech, and social behavior, with the most consistent evidence observed for bipolar disorder and depressive symptoms. However, external validation is uncommon, study samples are often small, missingness is frequently ignored, labels are temporally misaligned with sensor streams, and reported discrimination can therefore overestimate real-world performance. Edge AI offers lower latency, reduced network exposure, and bandwidth savings, but it introduces device constraints and does not by itself guarantee privacy. Privacy-preserving strategies should therefore combine data minimization, local feature extraction, strict retention limits, cryptographic separation of identity, access control, and auditable governance. Recent work on evidence conflict further shows that diagnostic AI may be sensitive to the order and presence of contradictory information, supporting the need for explicit contradiction detection, uncertainty calibration, abstention, and clinician escalation rather than probability accumulation alone. The central translational finding is that the components of the proposed Psychiatric Diagnostic Assistant Device (PDAD) are individually supported by adjacent evidence, but the specific combination of continuous behavioral biomarker extraction, privacy-preserving edge processing, contradiction-aware inference, and information-gain-driven adaptive sensor control has not yet been clinically validated as an integrated psychiatric diagnostic system. The most defensible next step is a staged prospective programme using independent reference standards, external validation, safety monitoring, privacy verification, and a clinical utility design aligned with current AI reporting and evaluation guidance.