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Machine learning has driven remarkable advances in many fields and, recently, it has been pivotal in enhancing the diagnosis and treatment of complex brain disorders. Biomedical and neuroscience studies frequently rely on neuroimaging as it provides non-invasive quantitative measurement of the structure and function of the nervous system. Machine and deep learning methods can, for example, refine findings for specific diseases or cohorts enabling the detection of imaging markers at an individual level. This, in turn, paves the way for personalized treatment plans. In this course, we explore the methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous neuroimaging data and study novel, robust, scalable, and interpretable machine learning models for this purpose.

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Over 900 million individuals worldwide suffer from a mental health disorder. Human and financial costs associated with the management of individuals with mental health disorder are substantial and constitute a growing public health challenge. Yet there are presently no objective markers used to determine which individuals have a mental health disorder and predict the progression of the disorder. Furthermore, there are presently a limited number of effective treatments for mental health disorders, as well as considerable heterogeneity in treatment response. The lack of access to mental health care is yet another challenge in developed as well as developing countries. Newly available technologies such as Artificial Intelligence offer an unprecedented opportunity for developing solutions that address the aforementioned challenges and problems. In this interdisciplinary seminar, students will learn about (i) psychopathology, (ii) state-of-the-art in diagnosis and treatments of mental health disorders, (iii) unaddressed challenges and problems related to mental health, (iv) artificial intelligence and its potential through real-world examples, (v) recent real-world applications of artificial intelligence that address the challenges and problems related to mental health, and (vi) ethical issues associated with the application of artificial intelligence to mental health. 

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The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence (AI), are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. However, few ever reach the patient - those with much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical know-how to integrate AI solutions into the medical system successfully. In this course, we tackle the central question: How can young students find feasible and impactful medical problems, and build, scale, and translate technology solutions into the clinic? Together, we will discover the transformative technologies of tomorrow that we can build today.

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