Use of AI for Detection, Diagnosis and Treatment of Mental Health Disorder
Arpan Soparkar (MBA '21, Berkeley HAAS School of Business) *[email protected]
Changhao Wei (MBA '21, Chicago Booth School of Business) *[email protected]u
Arpan Soparkar (MBA '21, Berkeley HAAS School of Business) *[email protected]
Changhao Wei (MBA '21, Chicago Booth School of Business) *[email protected]u
Thesis
With $321 million raised across 26 deals, the funding for the mental health and wellness sector reached record high levels in Q2 2019 (1), compared to $227 million invested in Q2 2018. However, majority of this funding was channeled in self-care apps (33%), telepsychiatry (16%), and provider tools (15%) segments, where few companies leverage latest technologies of artificial intelligence. Similarly, healthcare AI companies raised record high funding of $1.64 billion across 122 deals in Q3 2019, much higher compared to $762 million invested across 67 deals in Q3 2018 (2), with a focus on sectors such as infectious diseases, oncology and biomedical databases. In this paper, we explore in detail how the time is ripe for venture capital firms to invest in platforms and solutions that use artificial intelligence to address challenges of detection, diagnosis and treatment in mental health disorders.
Artificial Intelligence and Healthcare in the US: A Quick Overview
In computer science, artificial intelligence (AI) is intelligence demonstrated by machines. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. In recent years, AI has disrupted many industries, bringing about exponential growth - fueled by superhuman productivity and efficiency.
Among 13 industries studied (3) by Accenture their usage of AI, Healthcare ranks at the top. Healthcare spending in the US has grown faster than the economy for decades, resulting in growth of the healthcare share of national GDP from about 7 percent in 1970 to approximately 18 percent today (4). In the healthcare industry, the impact of artificial intelligence can be literally life changing. The total investment in healthcare AI is expected to reach $6.6B by 2021 (5), and is expected to result in annual savings of $150B by 2026 (6). Advancing human-computer interface to enhancing analytics precisions (7), and from surgical robots to drug discovery (8), AI offers a wide swathe of ways to improve healthcare. By its very nature, data input from every new patient improves predictions and accuracy of an AI model. Further details can be found in this paper, which explains how this feature makes AI an excellent differentiator in the field of healthcare diagnostics. Similarly, here is another informative paper on the use of AI in the cancer diagnostics vertical.
The State of Mental Health Crisis in the US
Societies have a complex relationship with mental health. This wellness sector presents a range of conditions that vary in degree of severity, ranging from mild to moderate to severe. Because of historic taboo associated with mental health disorders, these problems have grown rapidly yet silently. As of 2017, nearly one in five US adults (46.6 million) live with a mental illness. These conditions include, but are not limited to, ADHD, anxiety, depression, obsessive compulsive disorder (OCD), bipolar disorder, post-traumatic stress disorder (PTSD), substance abuse and various eating disorders.
According to breakdown statistics from National Institute of Mental Health (NIMH) (9), this mounting crisis affects the population differently. While 15.1% of men reported having a mental illness, this number was 22.3% among women. Among people who identify with 2 or more races, 28.6% reported having a mental illness, while this number was 20.4% among White adults, and 15.2% among Hispanic adults. The range is even wider, when considering adults who received mental health services. While 48% of White adults with AMI (Any Mental Illness) received mental health services, this number is much lower for Hispanics (32.6%), Blacks (30.6%) and especially, Asians (20.2%). Among others, some major factors causing this variety would be socio-economic conditions of the patients as well as social taboo against recognition and acknowledgement of the conditions, followed by seeking of medical help.
With $321 million raised across 26 deals, the funding for the mental health and wellness sector reached record high levels in Q2 2019 (1), compared to $227 million invested in Q2 2018. However, majority of this funding was channeled in self-care apps (33%), telepsychiatry (16%), and provider tools (15%) segments, where few companies leverage latest technologies of artificial intelligence. Similarly, healthcare AI companies raised record high funding of $1.64 billion across 122 deals in Q3 2019, much higher compared to $762 million invested across 67 deals in Q3 2018 (2), with a focus on sectors such as infectious diseases, oncology and biomedical databases. In this paper, we explore in detail how the time is ripe for venture capital firms to invest in platforms and solutions that use artificial intelligence to address challenges of detection, diagnosis and treatment in mental health disorders.
Artificial Intelligence and Healthcare in the US: A Quick Overview
In computer science, artificial intelligence (AI) is intelligence demonstrated by machines. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. In recent years, AI has disrupted many industries, bringing about exponential growth - fueled by superhuman productivity and efficiency.
Among 13 industries studied (3) by Accenture their usage of AI, Healthcare ranks at the top. Healthcare spending in the US has grown faster than the economy for decades, resulting in growth of the healthcare share of national GDP from about 7 percent in 1970 to approximately 18 percent today (4). In the healthcare industry, the impact of artificial intelligence can be literally life changing. The total investment in healthcare AI is expected to reach $6.6B by 2021 (5), and is expected to result in annual savings of $150B by 2026 (6). Advancing human-computer interface to enhancing analytics precisions (7), and from surgical robots to drug discovery (8), AI offers a wide swathe of ways to improve healthcare. By its very nature, data input from every new patient improves predictions and accuracy of an AI model. Further details can be found in this paper, which explains how this feature makes AI an excellent differentiator in the field of healthcare diagnostics. Similarly, here is another informative paper on the use of AI in the cancer diagnostics vertical.
The State of Mental Health Crisis in the US
Societies have a complex relationship with mental health. This wellness sector presents a range of conditions that vary in degree of severity, ranging from mild to moderate to severe. Because of historic taboo associated with mental health disorders, these problems have grown rapidly yet silently. As of 2017, nearly one in five US adults (46.6 million) live with a mental illness. These conditions include, but are not limited to, ADHD, anxiety, depression, obsessive compulsive disorder (OCD), bipolar disorder, post-traumatic stress disorder (PTSD), substance abuse and various eating disorders.
According to breakdown statistics from National Institute of Mental Health (NIMH) (9), this mounting crisis affects the population differently. While 15.1% of men reported having a mental illness, this number was 22.3% among women. Among people who identify with 2 or more races, 28.6% reported having a mental illness, while this number was 20.4% among White adults, and 15.2% among Hispanic adults. The range is even wider, when considering adults who received mental health services. While 48% of White adults with AMI (Any Mental Illness) received mental health services, this number is much lower for Hispanics (32.6%), Blacks (30.6%) and especially, Asians (20.2%). Among others, some major factors causing this variety would be socio-economic conditions of the patients as well as social taboo against recognition and acknowledgement of the conditions, followed by seeking of medical help.
The most shocking statistics emerge when we look beyond adults: 49.5% of American adolescents (aged 13-18) live with a mental health disorder, while an estimated 22.2% of them had severe impairment. According to CDC, many of these problems start very early (10): 1 in 6 U.S. children aged 2–8 years (17.4%) had a diagnosed mental, behavioral, or developmental disorder. To make matters worse, these conditions in children often occur together: For children aged 3-17 years with behavior problems, more than 1 in 3 also have anxiety (36.6%) and about 1 in 5 also have depression (20.3%).
While this is a major health concern for individuals, it also is a growing concern for employers. According to a 2018 Willis Towers Watson report (11), employees with mental health conditions like depression, bipolar disorder or substance abuse make six times as many emergency-room visits as the overall population, and submit two to four times as many medical claims. To put these numbers into perspective, an average employee submits $5,929 worth of medical claims annually, while an employee with depression submits claims of $14,967.
With rapidly rising costs, the affordability of healthcare in the US has become a hotly debated topic. The average hospital cost for a patient readmitted for a mood disorder is $7,100. Patients hospitalized with serious mental illness are much more likely to be readmitted in the next 30 days if they do not receive follow-up treatment. The National Alliance on Mental Health estimates that untreated mental illness costs the country up to $300 billion every year due to losses in productivity (12).
Along with financial costs of re-hospitalization and emergency care, there is a host of social and human costs at stake here. Mental health issues cost the U.S. economy close to $200 billion dollars in lost wages alone, plus about $100 billion in healthcare costs (13). According to NAMI (14), 46% of people who die by suicide had a diagnosed mental health condition. The overall suicide rate in the U.S. has increased by 31% since 2001. Suicide today is the 10th leading cause of death in America, costing the US $51 billion a year. According to a 2018 Lancet Commission report (15), mental disorders are on the rise in every country in the world and will cost the global economy $16 trillion by 2030. The economic cost is primarily due to early onset of mental illness and lost productivity, with an estimated 12 billion working days lost due to mental illness every year.
As this month of May is observed in the US as Mental Health Awareness Month, it happens to be an acutely relevant awareness signal, especially this year. As of mid-May, over 90,000 American lives and over 36 million American jobs have been lost, due to the Covid-19. As this unprecedented ‘Black Swan’ event ravages public health and the economy of our society, it directly threatens the mental health of millions. A study (16) published on March 23rd in the medical journal JAMA found that, among 1,257 healthcare workers working with COVID-19 patients in China, 50.4% reported symptoms of depression, 44.6% symptoms of anxiety, 34% insomnia, and 71.5% reported distress. Nurses and other frontline workers were among those with the most severe symptoms. A global survey of 2700 people by Qualtrics (17) revealed stark statistics: 67% of the responders experience higher stress levels, 57% report more anxiety, with 54% reporting emotional exhaustion.
AI for Detection, Diagnosis and Treatment of Mental Health Disorders
Shame and stigma around mental health conditions prevent about 80% of patients from seeking treatments (18). With its nonhuman nature and potential for accuracy, artificial intelligence can offer very impactful solutions. It is then not surprising that global mental health software market is expected to reach $4.58B in 2026 (19). Specifically, we focus on its applications in early detection, diagnosis and treatment verticals.
Detection: Popular ubiquitousness of social networks can dramatically aid in early detection of some of the mental health disorders. A study conducted by University of Pennsylvania’s Penn Medicine Center for Digital Health in October 2019 examines the Facebook postings of 683 patients during the months before they were diagnosed with depression. The AI tool they deployed looked for indicators like frequent use of words associated with sadness, hate and other emotions along with the frequent use of “I”. The researchers concluded that this approach was a good way of detecting depression ahead of the patient being clinically diagnosed (20). Using data from over 5000 patients with a history of self-harm or suicide attempts, the machine learning algorithm created at Vanderbilt University Medical Center, was 84 percent accurate at predicting whether someone would attempt suicide the following week, and 80 percent accurate at predicting suicide attempts over the following two years (21).
Diagnosis: With their highly qualitative nature, traditional diagnosis methods are prone to both Type 1 (False Positive) and Type 2 (False Negative) errors. AI-aided data analysis could help doctors make diagnoses more quickly and accurately, getting patients on the right course of treatment faster. An AI system capable of mining very large volumes of structured and unstructured data such as narrative text in electronic health records (EHR) and medical imaging data, such as IBM’s Watson AI (22) could deliver practical diagnosis utility in mental health disorder (23). In a research project with war veterans from Afghanistan, Ellie, a virtual therapist developed at The University of Southern California’s Institute for Creative Technologies, uncovered more evidence of post-traumatic stress disorder (PTSD) than the Post-Deployment Health Assessment administered by the military (24). In the field of neurodegenerative diseases, Optina Diagnostics, through its AI-enabled platform, employs hyperspectral imaging techniques to diagnose Alzheimer's disease, up to ten years before the symptoms of brain deterioration show up.
Treatment: Even though the treatment varies with the type of mental disorder, traditional treatment normally includes psychiatric counseling and some medications (25). With its ability to process vast amounts of empirical data, including multivariate relationships among complex factors, AI can greatly aid clinicians in deciding on optimal treatments and in measuring the efficacies over the duration of the treatments. Apps or other programs that incorporate AI could allow clinicians to monitor their patients remotely, alerting them to issues or changes that arise between appointments and helping them incorporate that knowledge into treatment plans (26). Quartet Health has provided an intelligent platform that links providers, payers and services together in its aim to more effectively deliver mental and primary care. Trayt is a promising startup in this realm, whose comorbidity assessment and outcomes tracking platform provides a 360-degree view of patients and enables 24/7 measure of outcomes. By aggregating previous treatment for current professionals in one place, Trayt improves compliance and reimbursements efficiency by 30% through integrated standard outcome measures. In its pilot program, 40% of patients reported improved care and clinician relationships. Trayt is chosen by the state of Texas as the official mental health platform; currently deployed in Children's Hospital, University Hospitals, Baylor College of Medicine and Bradley Hospital.
Advances in natural language processing have made chatbots the new starlets of AI for management of mental health care. Chatbots are constantly improving to become more human-like and natural. Companies like Ginger.io combine machine learning and a clinical network to provide patients with emotional support. Woebot, a free therapy chatbot developed by a clinical psychologist at Stanford, asks about patients’ mood and thoughts, “listens” to them, learns their feelings, and offers evidence-based cognitive behavior therapy (CBT) tools (27). Often maligned for their addictive nature, social networks can foster a sense of belonging and encourage positive communication. Many benefits of online health communities have already been widely recognized, scientists are now tapping into the potential AI can play in making people feel more connected socially.
DigitalDx Ventures©
DigitalDx Ventures is a majority women-owned impact venture firm, composed of a team of successful Silicon Valley digital health investors and medical professionals leveraging AI and big data technologies to address major global health issues (such as breast and other cancers, cardiovascular and kidney health, Alzheimer’s, and mental health). Collectively, the DigitalDx team has invested (directly or through previously managed funds) in over a dozen companies, which today have a combined value of well over $3 billion. The fund partners have had six exits (at 3x to 14x multiples) and have close to 200 patents to their credit. The firm is focused on investments that help doctors better understand their patients’ condition and make more personalized and effective treatment decisions. Both Arpan and Changhao currently serve as Fellows at DigitalDx Ventures because they were inspired by its mission.
While healthtech broadly is its forte, DigitalDx also boasts rich investment experience in the niche space of mental health. DigitalDx has an impressive portfolio with sectoral expertise in diagnostics, that includes Optina Diagnostics (a brain health company using multi-spectral imaging of the retina through a few second scan to diagnose Alzheimer's continuum and other dementia related diseases like arteriosclerosis and Lewy body dementia) and Trayt (a mental health company incorporating patient longitudinal data, comorbidity data and third party observation to enable physicians to more accurately diagnose and treat mental health). “DigitalDx will continue exploring more innovative ways to accurately diagnose mental health disorders, and personalize treatment, with an emphasis on early detection augmented by artificial intelligence and big data.” says Michele Colucci, CEO and co-founder of DigitalDx Ventures. Its team is a leading force behind many successful companies in this field. DigitalDx welcomes like minded investors to reach out and help them reinvent healthcare.
While this is a major health concern for individuals, it also is a growing concern for employers. According to a 2018 Willis Towers Watson report (11), employees with mental health conditions like depression, bipolar disorder or substance abuse make six times as many emergency-room visits as the overall population, and submit two to four times as many medical claims. To put these numbers into perspective, an average employee submits $5,929 worth of medical claims annually, while an employee with depression submits claims of $14,967.
With rapidly rising costs, the affordability of healthcare in the US has become a hotly debated topic. The average hospital cost for a patient readmitted for a mood disorder is $7,100. Patients hospitalized with serious mental illness are much more likely to be readmitted in the next 30 days if they do not receive follow-up treatment. The National Alliance on Mental Health estimates that untreated mental illness costs the country up to $300 billion every year due to losses in productivity (12).
Along with financial costs of re-hospitalization and emergency care, there is a host of social and human costs at stake here. Mental health issues cost the U.S. economy close to $200 billion dollars in lost wages alone, plus about $100 billion in healthcare costs (13). According to NAMI (14), 46% of people who die by suicide had a diagnosed mental health condition. The overall suicide rate in the U.S. has increased by 31% since 2001. Suicide today is the 10th leading cause of death in America, costing the US $51 billion a year. According to a 2018 Lancet Commission report (15), mental disorders are on the rise in every country in the world and will cost the global economy $16 trillion by 2030. The economic cost is primarily due to early onset of mental illness and lost productivity, with an estimated 12 billion working days lost due to mental illness every year.
As this month of May is observed in the US as Mental Health Awareness Month, it happens to be an acutely relevant awareness signal, especially this year. As of mid-May, over 90,000 American lives and over 36 million American jobs have been lost, due to the Covid-19. As this unprecedented ‘Black Swan’ event ravages public health and the economy of our society, it directly threatens the mental health of millions. A study (16) published on March 23rd in the medical journal JAMA found that, among 1,257 healthcare workers working with COVID-19 patients in China, 50.4% reported symptoms of depression, 44.6% symptoms of anxiety, 34% insomnia, and 71.5% reported distress. Nurses and other frontline workers were among those with the most severe symptoms. A global survey of 2700 people by Qualtrics (17) revealed stark statistics: 67% of the responders experience higher stress levels, 57% report more anxiety, with 54% reporting emotional exhaustion.
AI for Detection, Diagnosis and Treatment of Mental Health Disorders
Shame and stigma around mental health conditions prevent about 80% of patients from seeking treatments (18). With its nonhuman nature and potential for accuracy, artificial intelligence can offer very impactful solutions. It is then not surprising that global mental health software market is expected to reach $4.58B in 2026 (19). Specifically, we focus on its applications in early detection, diagnosis and treatment verticals.
Detection: Popular ubiquitousness of social networks can dramatically aid in early detection of some of the mental health disorders. A study conducted by University of Pennsylvania’s Penn Medicine Center for Digital Health in October 2019 examines the Facebook postings of 683 patients during the months before they were diagnosed with depression. The AI tool they deployed looked for indicators like frequent use of words associated with sadness, hate and other emotions along with the frequent use of “I”. The researchers concluded that this approach was a good way of detecting depression ahead of the patient being clinically diagnosed (20). Using data from over 5000 patients with a history of self-harm or suicide attempts, the machine learning algorithm created at Vanderbilt University Medical Center, was 84 percent accurate at predicting whether someone would attempt suicide the following week, and 80 percent accurate at predicting suicide attempts over the following two years (21).
Diagnosis: With their highly qualitative nature, traditional diagnosis methods are prone to both Type 1 (False Positive) and Type 2 (False Negative) errors. AI-aided data analysis could help doctors make diagnoses more quickly and accurately, getting patients on the right course of treatment faster. An AI system capable of mining very large volumes of structured and unstructured data such as narrative text in electronic health records (EHR) and medical imaging data, such as IBM’s Watson AI (22) could deliver practical diagnosis utility in mental health disorder (23). In a research project with war veterans from Afghanistan, Ellie, a virtual therapist developed at The University of Southern California’s Institute for Creative Technologies, uncovered more evidence of post-traumatic stress disorder (PTSD) than the Post-Deployment Health Assessment administered by the military (24). In the field of neurodegenerative diseases, Optina Diagnostics, through its AI-enabled platform, employs hyperspectral imaging techniques to diagnose Alzheimer's disease, up to ten years before the symptoms of brain deterioration show up.
Treatment: Even though the treatment varies with the type of mental disorder, traditional treatment normally includes psychiatric counseling and some medications (25). With its ability to process vast amounts of empirical data, including multivariate relationships among complex factors, AI can greatly aid clinicians in deciding on optimal treatments and in measuring the efficacies over the duration of the treatments. Apps or other programs that incorporate AI could allow clinicians to monitor their patients remotely, alerting them to issues or changes that arise between appointments and helping them incorporate that knowledge into treatment plans (26). Quartet Health has provided an intelligent platform that links providers, payers and services together in its aim to more effectively deliver mental and primary care. Trayt is a promising startup in this realm, whose comorbidity assessment and outcomes tracking platform provides a 360-degree view of patients and enables 24/7 measure of outcomes. By aggregating previous treatment for current professionals in one place, Trayt improves compliance and reimbursements efficiency by 30% through integrated standard outcome measures. In its pilot program, 40% of patients reported improved care and clinician relationships. Trayt is chosen by the state of Texas as the official mental health platform; currently deployed in Children's Hospital, University Hospitals, Baylor College of Medicine and Bradley Hospital.
Advances in natural language processing have made chatbots the new starlets of AI for management of mental health care. Chatbots are constantly improving to become more human-like and natural. Companies like Ginger.io combine machine learning and a clinical network to provide patients with emotional support. Woebot, a free therapy chatbot developed by a clinical psychologist at Stanford, asks about patients’ mood and thoughts, “listens” to them, learns their feelings, and offers evidence-based cognitive behavior therapy (CBT) tools (27). Often maligned for their addictive nature, social networks can foster a sense of belonging and encourage positive communication. Many benefits of online health communities have already been widely recognized, scientists are now tapping into the potential AI can play in making people feel more connected socially.
DigitalDx Ventures©
DigitalDx Ventures is a majority women-owned impact venture firm, composed of a team of successful Silicon Valley digital health investors and medical professionals leveraging AI and big data technologies to address major global health issues (such as breast and other cancers, cardiovascular and kidney health, Alzheimer’s, and mental health). Collectively, the DigitalDx team has invested (directly or through previously managed funds) in over a dozen companies, which today have a combined value of well over $3 billion. The fund partners have had six exits (at 3x to 14x multiples) and have close to 200 patents to their credit. The firm is focused on investments that help doctors better understand their patients’ condition and make more personalized and effective treatment decisions. Both Arpan and Changhao currently serve as Fellows at DigitalDx Ventures because they were inspired by its mission.
While healthtech broadly is its forte, DigitalDx also boasts rich investment experience in the niche space of mental health. DigitalDx has an impressive portfolio with sectoral expertise in diagnostics, that includes Optina Diagnostics (a brain health company using multi-spectral imaging of the retina through a few second scan to diagnose Alzheimer's continuum and other dementia related diseases like arteriosclerosis and Lewy body dementia) and Trayt (a mental health company incorporating patient longitudinal data, comorbidity data and third party observation to enable physicians to more accurately diagnose and treat mental health). “DigitalDx will continue exploring more innovative ways to accurately diagnose mental health disorders, and personalize treatment, with an emphasis on early detection augmented by artificial intelligence and big data.” says Michele Colucci, CEO and co-founder of DigitalDx Ventures. Its team is a leading force behind many successful companies in this field. DigitalDx welcomes like minded investors to reach out and help them reinvent healthcare.
(1) https://www.fiercehealthcare.com/tech/venture-capital-investment-ai-and-mental-health-startups-surges-q2-report
(2) https://www.cbinsights.com/research/report/healthcare-ai-in-numbers-q1-2020/
(3) https://www.accenture.com/_acnmedia/PDF-75/Accenture-Chart-Healthcare-Ranks-1-for-AI-Use.pdf#zoom=50
(4) https://altarum.org/solution/health-sector-spending
(5) https://www.forbes.com/sites/insights-intelai/2019/02/11/ai-and-healthcare-a-giant-opportunity/#23d2fd004c68
(6) https://www.accenture.com/t20171215T032059Z__w__/us-en/_acnmedia/PDF-49/Accenture-Health-Artificial-Intelligence.pdf#zoom=150
(7) https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare
(8) https://builtin.com/artificial-intelligence/artificial-intelligence-healthcare
(9) https://www.nimh.nih.gov/health/statistics/mental-illness.shtml
(10) https://www.cdc.gov/childrensmentalhealth/data.html
(11) https://www.cnbc.com/2018/09/26/employers-are-starting-to-think-about-healthy-differently.html
(12) https://www.constellationbehavioralhealth.com/blog/the-real-cost-of-untreated-mental-illness-in-america/
(13) https://www.nbcnews.com/better/health/why-aren-t-mental-health-screenings-part-our-annual-physicals-ncna839226
(14) https://nami.org/mhstats
(15) https://www.thelancet.com/commissions/global-mental-health
(16) https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2763229
(17) https://www.qualtrics.com/blog/confronting-mental-health/
(18) https://www.bloomberg.com/news/articles/2019-11-13/mental-health-is-still-a-don-t-ask-don-t-tell-subject-at-work
(19) https://www.globenewswire.com/news-release/2019/01/04/1680465/0/en/Global-Mental-Health-Software-Market-Will-Reach-USD-4-585-Million-by-2026-Zion-Market-Research.html
(20) https://www.governmentciomedia.com/ai-programs-can-help-early-detection-mental-health-issues
(21) https://medicalfuturist.com/artificial-intelligence-in-mental-health-care/
(22) https://www.psychologytoday.com/us/blog/integrative-mental-health-care/201910/artificial-intelligence-ai-and-mental-health-care
(23) https://www.ncbi.nlm.nih.gov/pubmed/?term=The+emerging+agenda+of+stratified+medicine+in+neurology
(24) https://hbr.org/2018/10/ais-potential-to-diagnose-and-treat-mental-illness
(25) https://www.mayoclinic.org/diseases-conditions/mental-illness/diagnosis-treatment/drc-20374974
(26) https://time.com/5727535/artificial-intelligence-psychiatry/
(27) https://www.verywellhealth.com/using-artificial-intelligence-for-mental-health-4144239
(2) https://www.cbinsights.com/research/report/healthcare-ai-in-numbers-q1-2020/
(3) https://www.accenture.com/_acnmedia/PDF-75/Accenture-Chart-Healthcare-Ranks-1-for-AI-Use.pdf#zoom=50
(4) https://altarum.org/solution/health-sector-spending
(5) https://www.forbes.com/sites/insights-intelai/2019/02/11/ai-and-healthcare-a-giant-opportunity/#23d2fd004c68
(6) https://www.accenture.com/t20171215T032059Z__w__/us-en/_acnmedia/PDF-49/Accenture-Health-Artificial-Intelligence.pdf#zoom=150
(7) https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare
(8) https://builtin.com/artificial-intelligence/artificial-intelligence-healthcare
(9) https://www.nimh.nih.gov/health/statistics/mental-illness.shtml
(10) https://www.cdc.gov/childrensmentalhealth/data.html
(11) https://www.cnbc.com/2018/09/26/employers-are-starting-to-think-about-healthy-differently.html
(12) https://www.constellationbehavioralhealth.com/blog/the-real-cost-of-untreated-mental-illness-in-america/
(13) https://www.nbcnews.com/better/health/why-aren-t-mental-health-screenings-part-our-annual-physicals-ncna839226
(14) https://nami.org/mhstats
(15) https://www.thelancet.com/commissions/global-mental-health
(16) https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2763229
(17) https://www.qualtrics.com/blog/confronting-mental-health/
(18) https://www.bloomberg.com/news/articles/2019-11-13/mental-health-is-still-a-don-t-ask-don-t-tell-subject-at-work
(19) https://www.globenewswire.com/news-release/2019/01/04/1680465/0/en/Global-Mental-Health-Software-Market-Will-Reach-USD-4-585-Million-by-2026-Zion-Market-Research.html
(20) https://www.governmentciomedia.com/ai-programs-can-help-early-detection-mental-health-issues
(21) https://medicalfuturist.com/artificial-intelligence-in-mental-health-care/
(22) https://www.psychologytoday.com/us/blog/integrative-mental-health-care/201910/artificial-intelligence-ai-and-mental-health-care
(23) https://www.ncbi.nlm.nih.gov/pubmed/?term=The+emerging+agenda+of+stratified+medicine+in+neurology
(24) https://hbr.org/2018/10/ais-potential-to-diagnose-and-treat-mental-illness
(25) https://www.mayoclinic.org/diseases-conditions/mental-illness/diagnosis-treatment/drc-20374974
(26) https://time.com/5727535/artificial-intelligence-psychiatry/
(27) https://www.verywellhealth.com/using-artificial-intelligence-for-mental-health-4144239
