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This is a Quantitative Study study (also classified as: Experimental Study).

September 12, 2025

The Role of Artificial Intelligence in Early Detection and Management of Anxiety and Depression Disorders

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Models based on transformer technology showed superior accuracy in detecting anxiety and depression, and demonstrated how AI can enhance mental health diagnostics.
With a performance accuracy of 0.93 and AUC-ROC of 0.96, Roberta outperformed traditional machine learning models in diagnosing mental health disorders.
The study utilized multimodal data, including text, speech, and physiological measures, emphasizing the importance of combining various data types for better outcomes.
Despite promising results, limitations regarding data heterogeneity and ethical issues must be acknowledged as future studies aim to refine AI applications.

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Implication

This analysis shows AI improves diagnostic reliability for anxiety and depression through multimodal data fusion, suggesting its future in mental health care.

Cite This Study

Bibi et al. (2025) studied this question.

synapsesocial.com/papers/68d44a3731b076d99fa53522 https://doi.org/10.59075/rjs.v3i3.227

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Implication

This analysis shows AI improves diagnostic reliability for anxiety and depression through multimodal data fusion, suggesting its future in mental health care.

The Role of Artificial Intelligence in Early Detection and Management of Anxiety and Depression Disorders

Key Points

Discussion

Implication

Cite This Study

The Role of Artificial Intelligence in Early Detection and Management of Anxiety and Depression Disorders

Key Points

Discussion

Implication

Cite This Study

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