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This review aims to synthesize recent advancements in AI for the detection and monitoring of depression.

July 23, 2026Open Access

Artificial Intelligence for Depression Detection: A Review of Classical, Multimodal, and Explainable Systems

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This review aims to synthesize recent advancements in AI for the detection and monitoring of depression.
Reviewed various AI methods for depression detection, including classical machine learning and deep learning approaches.
Analyzed multimodal data sources such as text, speech, facial expressions, and physiological signals.
Discussed the implications of explainable AI techniques for predictive performance and clinical use.
Found that AI systems can enhance the early detection of depression through various data modalities.
Identified significant research gaps in dataset diversity and ethical AI that need addressing for clinical application.
Concluded that AI should support clinicians rather than replace them in diagnosing depression.

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Review synthesizes AI-driven depression detection across modalities, highlighting implications for clinical deployment.

Saransh Tripathi (2026) studied this question.

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