Accepted for/Published in: JMIR AI
Date Submitted: Dec 19, 2025
Open Peer Review Period: Dec 22, 2025 - Feb 16, 2026
Date Accepted: Jun 12, 2026
(closed for review but you can still tweet)
MoodMon system based on artificial intelligence – an innovative clinical tool for affective disorders.
ABSTRACT
Background:
Psychiatry needs objective technological tools to address global staffing shortages, stigma, and other systemic challenges. A long-term, naturalistic study using AI to effectively detect changes in mental state in major depressive disorder (MDD) and bipolar disorder (BD) based on physical characteristics of the voice represents a breakthrough in biomarker validation. The MoodMon system was developed along with a mobile application for smartphones.
Objective:
The aim of the study was to determine whether physical voice parameters would be effective as biomarkers of mental status changes in affective disorders and whether they would be useful in remote clinical monitoring of patients by psychiatrists.
Methods:
To evaluate the effectiveness of artificial intelligence (AI) algorithms in detecting changes in mental state based on physical voice parameters, data from 75 patients diagnosed with bipolar disorder (BD) and 25 patients with major depressive disorder (MDD) for 944 days were used. This makes this the longest analysis in the world covering two of the most common mental disorder diagnoses. A wealth of clinical, behavioral, and technical data was collected and used to train the MoodMon machine learning system under the supervision of human experts- experienced psychiatrists. The AI module consists of an ensemble of selected supervised learning and clustering algorithms In the first stage, the AI was trained using objective data and clinical assessments conducted by psychiatrists, including 17-item versions of the HDRS and YMRS, as well as the CGI scale. The second stage involved further refinement of the AI using individual and population data and generating alerts when subtle changes in mental state were detected.
Results:
19 of the 243 specific physical voice parameters tested were found to be most effective in detecting changes in mental status. The system demonstrated high performance, achieving the following sensitivity (true positive rate – TPR) and specificity (true negative rate – TNR) values for both diagnoses: TPR = 89.5%, TNR = 98.8%; BD: TPR = 89.6%, TNR = 98.9%; MDD: TPR = 89.1%, TNR = 98.5%. Voice alerts in the MoodMon system are a key tool supporting clinical decision-making. They increase the probability of a clinical visit and exert a significant influence on the likelihood of treatment modification.
Conclusions:
The system confirmed the presence of parameters that may serve as biomarkers of mental state changes in bipolar disorder (BD) and major depressive disorder (MDD). A key clinical implication is the increased probability of prompt treatment modification following an alert, thereby supporting the primary objective underlying the development of the MoodMon AI tool. Clinical Trial: Study: UR.D.WM.DNB.39.2021; Funder: National Centre for Research and Development, Poland. Project title: Development of a system supporting the monitoring of the course and early detection of relapses of affective disorders based on artificial intelligence algorithms. Agreement: POIR.01.01.01-00-0342/20
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