Abstract
The accurate diagnosis of mental illness is challenging because mental illness does not result in evident physical symptoms as compared to physical illness like the common cold. As a result, no definitive medical tests exist for mental illnesses. This situation is further aggravated by the fact that many of the symptoms of various mental illnesses overlap. Further, traditional means of mental care and therapy are not easily accessible to a majority of the population in developed and developing countries alike. In addition, openly discussing mental illness in major parts of society is still considered taboo. Therefore, a plausible way to improve mental illness diagnosis and address the aforementioned challenges is by using Artificial Intelligence (AI). This paper presents a comprehensive survey of AI-enabled approaches to Major Depressive Disorder (MDD) diagnosis and outlines some future research directions and challenges in this field. The paper also presents a preliminary system architecture of an AI-enabled approach to diagnose mental health with the objectives of making the underlying system more user-centric, scalable and accessible.
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Oyeniyi, O., Dhandhukia, S.S., Sen, A., Fletcher, K.K. (2022). A Study of Artificial Intelligence Frameworks and Their Capability to Diagnose Major Depressive Disorder. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2021 Workshops. ICSOC 2021. Lecture Notes in Computer Science, vol 13236. Springer, Cham. https://doi.org/10.1007/978-3-031-14135-5_1
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