JMIR Publications

ABSTRACT

Artificial intelligence (AI)-assisted mental health systems increasingly process clinical notes, speech and language features, response latency, digital phenotyping signals, ecological momentary assessment, and patient-generated data. A recurrent safety risk is that surface disorganization--losing the thread, blankness, reduced coherence, withdrawal, task chaos, long latency, or threat-saturated language--may be mapped directly onto high-stakes labels such as relapse, nonadherence, negative symptoms, psychotic deterioration, risk, or stable cognitive deficit. This Viewpoint proposes an auditable, uncertainty-aware representation layer for AI-assisted mental health workflows. The framework separates observable cues from candidate state representation, clinical safeguards, human-final review, and downstream action. A compact set of candidate coordinates distinguishes current load, executive-reflective capacity, threat attribution, reality-testing stability, and thought-form organization while preserving alternative explanations and uncertainty. The framework does not introduce a new disorder, diagnostic instrument, validated scale, risk score, clinical guideline, or autonomous AI system. It does not claim that stress, anxiety, trauma, or overload explains psychosis, formal thought disorder, delirium, mania, catatonia, medication effects, substance-related states, suicidality, violence risk, or functional collapse. Clinical safeguards for medical, medication-related, substance-related, sleep-related, psychosis-spectrum, mania, catatonia, suicidality, violence-risk, neurological, and functional-collapse pathways constrain any downstream use. A staged validation roadmap is outlined: concept bounds, codebook development, interrater reliability, within-person validity, response-to-pacing analyses, prediction and calibration only after safety gates, implementation outcomes, patient experience, and safety monitoring. The goal is not faster psychiatric labeling but safer interpretability in digital mental health: AI should help clinicians preserve context, uncertainty, safeguards, and re-check windows before surface cues become consequential labels.