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Fatemeh Abbasi1 
, Niloufar Delfan2 , Fatemeh Zokayie3 , Mina Bayat Sefidi4 , Mahsa Ghanbarian5 , Arezou Ahmadi6 , Forugh Salajegheh7 , Mahdi Jafari Asl *8
, Niloufar Delfan2 , Fatemeh Zokayie3 , Mina Bayat Sefidi4 , Mahsa Ghanbarian5 , Arezou Ahmadi6 , Forugh Salajegheh7 , Mahdi Jafari Asl *8
1- Student Research Committee, Faculty of Medicine, Mazandaran University of Medical Sciences, Mazandaran, Iran.
2- Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran.
3- Faculty of Medical science, Karaj Branch, Islamic Azad university, Karaj, Iran.
4- Department of Psychology, Faculty of Humanities, North Tehran Branch, Islamic Azad University, Tehran, Iran.
5- Department of Health Psychology, Faculty of Medical Sciences, Gorgan Branch, Islamic Azad University, Gorgan, Iran.
6- Department of Clinical Psychology, Faculty of Medical Sciences, Torbat-e jam Branch, Islamic Azad University, Torbat-e jam, Iran.
7- Department of Psychology, Ayatollah Amoli International Branch, Islamic Azad University, Amol, Mazandaran, Iran.
8- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
2- Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran.
3- Faculty of Medical science, Karaj Branch, Islamic Azad university, Karaj, Iran.
4- Department of Psychology, Faculty of Humanities, North Tehran Branch, Islamic Azad University, Tehran, Iran.
5- Department of Health Psychology, Faculty of Medical Sciences, Gorgan Branch, Islamic Azad University, Gorgan, Iran.
6- Department of Clinical Psychology, Faculty of Medical Sciences, Torbat-e jam Branch, Islamic Azad University, Torbat-e jam, Iran.
7- Department of Psychology, Ayatollah Amoli International Branch, Islamic Azad University, Amol, Mazandaran, Iran.
8- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Abstract:
Background: Depression is a widespread and multifaceted mental health disorder that profoundly affects quality of life and productivity. It is characterized by persistent sadness, loss of interest, and various physical and cognitive symptoms. Traditional diagnostic approaches often rely on subjective assessments, resulting in delayed or inaccurate diagnoses, and typically lack personalization in treatment planning.
Objective: This study investigates recent advancements in artificial intelligence (AI) for the diagnosis, treatment, and monitoring of depression. The integration of deep learning (DL) algorithms with multimodal data sources, including genetic, behavioral, neuroimaging, and digital signals, offers new opportunities to enhance clinical decision-making and develop more precise, individualized interventions.
Methods: A comprehensive literature review was conducted focusing on AI-driven techniques, including machine learning (ML), deep learning, and natural language processing (NLP). These were applied to multimodal datasets such as neuroimaging (e.g., EEG, fMRI, fNIRS), genetic profiles, wearable sensor data (e.g., heart rate, sleep patterns), and behavioral indicators (e.g., voice, facial expressions, social media use). AI architectures examined include CNNs, Recurrent Neural Networks (RNNs), LSTMs, and Transformer-based models.
Results: AI has demonstrated high accuracy (85–90%) in detecting depressive states and predicting treatment outcomes. Wearable-AI systems enable continuous mood monitoring and early relapse detection, while deep learning models outperform traditional diagnostic tools across various datasets.
Conclusion: AI is redefining depression care by supporting scalable, timely, and personalized solutions. However, challenges remain, including model interpretability, data privacy, and clinical validation. Future work must focus on ethically designed, explainable, and robust AI systems to ensure safe deployment in clinical practice.
Objective: This study investigates recent advancements in artificial intelligence (AI) for the diagnosis, treatment, and monitoring of depression. The integration of deep learning (DL) algorithms with multimodal data sources, including genetic, behavioral, neuroimaging, and digital signals, offers new opportunities to enhance clinical decision-making and develop more precise, individualized interventions.
Methods: A comprehensive literature review was conducted focusing on AI-driven techniques, including machine learning (ML), deep learning, and natural language processing (NLP). These were applied to multimodal datasets such as neuroimaging (e.g., EEG, fMRI, fNIRS), genetic profiles, wearable sensor data (e.g., heart rate, sleep patterns), and behavioral indicators (e.g., voice, facial expressions, social media use). AI architectures examined include CNNs, Recurrent Neural Networks (RNNs), LSTMs, and Transformer-based models.
Results: AI has demonstrated high accuracy (85–90%) in detecting depressive states and predicting treatment outcomes. Wearable-AI systems enable continuous mood monitoring and early relapse detection, while deep learning models outperform traditional diagnostic tools across various datasets.
Conclusion: AI is redefining depression care by supporting scalable, timely, and personalized solutions. However, challenges remain, including model interpretability, data privacy, and clinical validation. Future work must focus on ethically designed, explainable, and robust AI systems to ensure safe deployment in clinical practice.
Keywords: Artificial Intelligence, Major Depressive Disorder, Machine Learning, Clinical Decision Support, Mental Health Technology, Deep Learning, Precision Psychiatry
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