The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in Psychiatry - Detection Applications, Specifically Depression Disorder: A Brief Review

Aug 2, 2026

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The COVID-19 pandemic has increased mental illness burden, especially depression, creating demand for diagnosis that is both accurate and fast while also reducing severe outcomes such as suicide. Machine learning offers speed and performance for psychiatry detection tasks, but clinical use requires clarity about decision drivers and patient-relevant explanations. This brief review surveys studies on machine learning and interpretable AI in psychiatry, summarizing the advantages and limitations to support better treatment and responsible deployment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

The use of machine learning in diagnosing mental illnesses

Machine learning methods

Ethical considerations and explainability

The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods In Psychiatry Detection Applications, Specifically Depression Disorder: A Brief Review. Hossein Simchi 1, Samira Tajik 1 1- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran. Corresponding Email: [h.simchi@alumni.sbu.ac.ir](h.simchi@alumni.sbu.ac.ir) Abstract The COVID-19 pandemic has forced many people to limit their social activities, which has resulted in a rise in mental illnesses, particularly depression. To diagnose these illnesses with accuracy and speed, and prevent severe outcomes such as suicide, the use of machine learning has become increasingly important. Additionally, to provide precise and understandable diagnoses for better treatment, AI scientists and researchers must develop interpretable AI-based solutions. This article provides an overview of relevant articles in the field of machine learning and interpretable AI, which helps to understand the advantages and disadvantages of using AI in psychiatry disorder detection applications. Keywords: Artificial Intelligence, Deep Learning, Machine Learning, Healthcare, Psychiatry Disorders, Mental Illnesses, Interpretable AI, COVID-19. 1- Introduction Maintaining good mental health is essential for an individual's overall well-being. It has a significant impact on the quality of life and should be given utmost importance. Therefore, it is crucial to understand the nature of mental illness and treat it as early as possible. Moreover, medical practitioners recognize the importance of mental health in an individual's overall well-being and its impact on society. They strive to identify and treat illnesses early with high accuracy. Studies have shown that the COVID-19 pandemic has led to a significant increase in mental health issues, particularly anxiety and depression [1-4] . The emergence of this disease has significantly increased the prevalence of anxiety, depression, post-traumatic stress disorder (PTSD), psychological distress, and stress in some countries around the world [5] . In addition, machine learning has played an increasingly important role in medical applications due to its high speed and accuracy [6, 7] . However, the use of machine learning in mental illnesses has not been fully investigated. It remains to be answered whether machine learning can be useful in detecting mental illnesses for clinical purposes. Also, ethical considerations must be taken into account when using machine learning. These include bias, transparency, and explainability [8, 9] . First, to train machine learning models, it is crucial to carefully select data to ensure that the model doesn't overfit. Second, patients and physicians require clear and transparent explanations for the model's output, leading to interpretable artificial intelligence (XAI) . Finally, this study aims to review the pros and cons of using machine learning and interpretable machine learning methods in detecting mental disorders, specifically depression. The article includes the following section: -Section 2 explains the use of machine learning for diagnosing mental illnesses. Notably, to write a useful review article, the study has been selected recent articles and relevant topics. 2-The use of machine learning in diagnosing mental illnesses Machine learning algorithms can be classified into two categories: traditional and deep learning algorithms. In traditional algorithms, feature engineering is manually performed using different methods such as random forest. In contrast, deep learning-based methods automate feature engineering using deep neural networks, which increases both speed and accuracy. Due to the high speed and accuracy of machine learning algorithms, particularly those based on deep learning, many neural network-based approaches have been used to diagnose psychiatric disorders in recent years [10-18] . There are various applications of m

FAQs

Why has machine learning become important for depression detection during the COVID-19 pandemic?

The pandemic increased mental health problems, particularly depression. Machine learning supports accurate and rapid diagnosis to help prevent severe outcomes such as suicide.

What are the main benefits of using machine learning for diagnosing and treating psychiatric disorders?

Benefits include smart diagnostic tools, symptom monitoring, personalized treatment recommendations, visualization for decision support, early diagnosis, improved long-term management, and explanations for disease causes and drug choices.

How do traditional machine learning methods differ from deep learning methods in psychiatric diagnosis?

Traditional methods often rely on manual feature engineering, while deep learning automates feature learning using deep neural networks, typically improving speed and accuracy.

Aug 2, 2026

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