Diagnostics
It is known that mental disorders are difficult to diagnose. At present, diagnosis is based on the display of symptoms categorized into mental health disorders by professionals and collected in the Diagnostic and Statistical Manual of Mental Disorders (the DSM). However, in many cases, with the current lack of biomarkers, and symptoms gathered through observations, such symptoms overlap among different diagnoses. Besides, humans are prone to inaccuracy and subjectivity: what is three in one person’s scale of anxiety might be seven for another.
One possible way for AI to assist or even replace human experts, as offered by the Virginia Tech group, is to combine the neuroimaging of fMRI with a trove of data, including survey responses, functional and structural MRIs, behavioral data, speech data from interviews, and psychological assessments. Another example is s Quartet Health, which screens patient medical histories and behavioral patterns to uncover undiagnosed mental health problems. To illustrate the concept, Quartet can flag possible anxiety based on whether someone has been repeatedly tested for a non-existent cardiac problem.
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AI can help researchers discover physical symptoms of mental disorders and track within the body the effectiveness of various interventions. Besides, it might find new patterns in our social behaviors, or see where and when a certain therapeutic intervention is effective, providing a template for preventative mental health treatment.
Treatment assistance
Similar to somatic diseases, AI algorithms can be used to evaluate the treatment of mental disorders, predict the course of the disease and help select the optimal treatment path. Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific medicine of line of treatment.
On example of using machine learning is application of algorithms to predict the specific antidepressant with the best chance of success. While clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant, the treatment efficacy can be improved by matching patients to interventions.
Beyond analyzing fMRI images, computational psychiatry faces, ethical, spiritual, practical, and technological issues. For instance, the huge stores of intensely personal data necessary for the algorithms, immediately raise the issue of cybersecurity. At the same time, however, it is a barrier between the individual, the personal data, and the counselor that can help overcome patients’ fear of stigmatizing and the reluctance to turn to help. […]
In recent years we have been hearing a lot about the potential of digital doctors and nurses: the example of AI becoming directly in charge of our welfare.
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But what if the technology turns to the mental health and digitalizes not physicians, but psychologists? The implications all favor to introduction of AI into the sphere:one fourth of adult population is estimated to be affected by mental disorders.According to the World Health Organization, depression alone afflicts roughly 300 million people around the globe. The sad truth is not all of them can reach out for help. The obstacles are related to the still existing stigma in the society, the lack of therapists, the price of the therapy, and — in some countries — the qualification of the specialists.
It looks like AI offers multiple opportunities to help people maintain and improve their mental health. At present, the most prospective domains for application of AI techniques are computational psychiatry and the development of specialized chatbots that could render counseling and therapeutic services
Computational psychiatry
Broadly defined, computational psychiatry encompasses two approaches: data-driven and theory-driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. Theory-driven approaches use models that instantiate prior knowledge of such mechanisms at multiple levels of analysis and abstraction. Computational psychiatry combines multiple levels and types of computation with multiple types of data to improve understanding, diagnostics, prediction and treatment of mental disorders.
Diagnostics
It is known that mental disorders are difficult to diagnose. At present, diagnosis is based on the display of symptoms categorized into mental health disorders by professionals and collected in the Diagnostic and Statistical Manual of Mental Disorders (the DSM). However, in many cases, with the current lack of biomarkers, and symptoms gathered through observations, such symptoms overlap among different diagnoses. Besides, humans are prone to inaccuracy and subjectivity: what is three in one person’s scale of anxiety might be seven for another.
One possible way for AI to assist or even replace human experts, as offered by the Virginia Tech group, is to combine the neuroimaging of fMRI with a trove of data, including survey responses, functional and structural MRIs, behavioral data, speech data from interviews, and psychological assessments. Another example is s Quartet Health, which screens patient medical histories and behavioral patterns to uncover undiagnosed mental health problems. To illustrate the concept, Quartet can flag possible anxiety based on whether someone has been repeatedly tested for a non-existent cardiac problem.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
AI can help researchers discover physical symptoms of mental disorders and track within the body the effectiveness of various interventions. Besides, it might find new patterns in our social behaviors, or see where and when a certain therapeutic intervention is effective, providing a template for preventative mental health treatment.
Treatment assistance
Similar to somatic diseases, AI algorithms can be used to evaluate the treatment of mental disorders, predict the course of the disease and help select the optimal treatment path. Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific medicine of line of treatment.
On example of using machine learning is application of algorithms to predict the specific antidepressant with the best chance of success. While clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant, the treatment efficacy can be improved by matching patients to interventions.
Beyond analyzing fMRI images, computational psychiatry faces, ethical, spiritual, practical, and technological issues. For instance, the huge stores of intensely personal data necessary for the algorithms, immediately raise the issue of cybersecurity. At the same time, however, it is a barrier between the individual, the personal data, and the counselor that can help overcome patients’ fear of stigmatizing and the reluctance to turn to help. […]
Read more – medium.com
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