What question did this study set out to answer?

The aim is to evaluate the efficacy of AI systems for detecting and treating depression over the last decade.

June 27, 2026Open Access

Multi‐Modal AI Approach in Depression Detection and Treatment: A Systematic Review of Last Decade

Q: What does this research mean for the field?

Artificial intelligence, particularly machine learning and deep learning models, demonstrates promising accuracy in predicting depression onset, severity, and treatment response using diverse multi-modal data sources. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Key Points

The aim is to evaluate the efficacy of AI systems for detecting and treating depression over the last decade.
Conducted a systematic review of 180 journal articles using PRISMA guidelines.
Analyzed various AI detection modalities, including machine learning and deep learning approaches.
Examined diverse data sources such as electroencephalogram and electronic healthcare records.
AI models show promising accuracy in predicting depression onset, severity, and treatment responses.
Multiple modalities including physiological indicators and healthcare records enhance depression detection.
Identified research challenges in automated depression diagnosis and highlighted future development prospects.

Abstract

ABSTRACT Depression is a common and devastating mental health illness with serious personal and societal consequences. Despite advancing treatment techniques, there are still hurdles in the effective diagnosis and treatment of depression, such as prompt diagnosis, personalized medication, and continuous monitoring. In recent years, artificial intelligence (AI) has emerged as a potential tool in mental health treatment, providing novel solutions to these difficulties. This systematic study aims to comprehensively assess the existing AI systems for depression detection and treatment. The paper presents a systematic and comprehensive review of the last decade for depression detection, prediction, and treatment. One hundred eighty journal articles fulfilling preset inclusion criteria were found and analyzed using Preferred Reporting Items for Systematic Reviews and Meta‐Analyses from major academic databases. This review used a variety of detection modalities (physical, physiological, repetitive transcranial magnetic stimulation, and pharmacological treatment response) and AI approaches, including machine learning (ML) and deep learning (DL), to address various areas of depression care, including detection, diagnosis, prediction, and treatment. Key findings demonstrate that AI offers tremendous promise in boosting depression care across the continuum, from early identification to individualized therapy optimization and remote monitoring. ML and DL models demonstrate promising accuracy in predicting depression onset, severity, and treatment response based on diverse data sources, including electroencephalogram, electrocardiogram, photoplethysmography, electrodermal activity, electronic healthcare records, facial, speech, text, and pharmaceutical data. The paper highlights the important research challenges in current automated depression decision‐making models. Finally, we emphasize the prospects for developing effective and robust AI‐based depression models incorporating data and model fusion, the model's trust, portability, privacy preservation, and security features. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning Technologies > Artificial Intelligence

Multi‐Modal AI Approach in Depression Detection and Treatment: A Systematic Review of Last Decade

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