AI in Mental Health: Transforming Diagnosis and Management of Depression and Anxiety

Mental health disorders, especially depression and anxiety, are major contributors to the global disease burden. Traditional psychiatric methods can be time-consuming and often struggle with accurate diagnosis and effective treatment. Artificial intelligence (AI) has the potential to improve diagnostic precision and streamline the management of mental health issues.

This review investigates AI's role in addressing mental health challenges, with a focus on anxiety and depression. Relevant literature was sourced from Medline, Google Scholar, and PubMed, using keywords like "artificial intelligence," "mental health," "depression," and "anxiety," emphasizing studies involving Asian populations. The search included English-language articles, which were screened based on titles and abstracts.

AI applications in psychiatry, including chatbots and wearable devices, enable early detection and individualized care. Machine learning and deep learning models that use data from sources such as social media and sensors assist in diagnosing and monitoring mental health conditions. Although these tools provide valuable support, ethical concerns related to privacy, algorithmic bias, and limitations in detecting suicidal ideation need to be addressed.

AI shows promise in transforming mental health care by increasing diagnostic speed, accuracy, and accessibility. Despite existing challenges, particularly around ethical considerations and acceptance in older populations, further research and careful regulation could allow AI to complement human-centered psychiatric care. Future studies should work to maximize the benefits of AI while preserving the critical human connection in mental health services.