Artificial Intelligence in Mental Health Research: Opportunities and Challenges

Abstract

Abstract Artificial intelligence (AI) involves use of machines and software to perform tasks that would typically require human like intelligence, such as natural language understanding and pattern recognition. With regards to healthcare, AI improves treatment predictions and research quality. Psychiatry which is facing a shortage of professionals and high mental illness rates could benefit significantly from AI, making focused research essential for advancing mental health care. AI integration into psychiatric research opens the doors to a vast number of opportunities. AI-based tools can effectively analyze large volumes of multidimensional and multimodal data in digital psychiatry research. Machine learning and neural networks can develop better prediagnostic screening tools and risk modeling to determine an individual’s susceptibility to, or risk of developing, a psychiatric disorder. However, several implementation challenges exist in effective integration of AI in its current form with clinical psychiatry and psychiatric research, including limited reliability in identifying subtle nuances of verbal and nonverbal communication, limitations in rapport building, handling of qualitative data and misgivings among clients and doctors. With this review, we aim to discuss the relevance of AI in mental health in current scenario and shed some light on challenges associated.

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