Mental health has become a major issue in today’s world, with more people than ever before suffering from depression, anxiety, and other mental health conditions. According to the recent 2022 report by the WHO, nearly one billion people — including 14% of the world’s adolescents — were living with a mental disorder in 2019. The depression and anxiety rates were estimated to have increased by 25% during the pandemic. In addition, currently, around half the world’s population is living in countries where there is a single psychiatrist serving 200,000 or more people (WHO, 2022). Current mental health systems are not able to keep up with the growing number. Around 71% of people with mental health issues do not receive mental health services (WHO, 2022). Additionally, other barriers within mental health care have been identified, such as social stigma, treatment access, long-waiting lists, and costs.
To cope with the growing need for mental health support, states and companies turned to the advancement of technology — Artificial Intelligence (AI), machine learning, and deep learning algorithms (Garg & Glick, 2018). These technologies are thought to assist the existing mental health workforce and also improve current industry conditions. The scientific interest in artificial intelligence to promote (mental) health is immense (Topol et al., 2019). Mental health is a peculiar field that involves emotional intelligence, empathy, and perspective-taking, yet experts agree that AI can positively impact the mental health industry in several different ways (Ducharme, 2019). There is especially a growing number of studies on AI-based chatbots (Dale, 2016; Brandtzaeg & Følstad, 2017). They are increasingly discussed in clinical psychology and psychotherapy for mental health promotion as an innovative application and “next generation” or “high-performance medicine” (Topol et al., 2019; Bendig et al., 2019).
One question that is often asked is what are (dis)advantages of digital mental health solutions? Drawing on a few studies, there are some dimensions of AI solutions that stand out:
Availability
One of the most popular advantages of AI-powered solutions, and in particular chatbots, is the factor of availability, such as quick response time, 24-hour availability, and ease of communication (Asar, 2021). Moreover, there is a potential for digital health to increase access to mental health services for everyone — meaning anyone owning a digital device would be able to use it. Digital health solutions are especially helpful for people who cannot access mental health care for financial, geographic, or other reasons like social anxiety. For young adults, in particular, automated online interventions are discussed as low-cost, interactive, and effective solutions for reducing anxiety and depression (Fitzpatrick et al., 2017; Frazier et al., 2016; Richardson et al., 2018). To date, artificial intelligence has hardly been used for counseling in the social domain, although there is a high and urgent need for accessible communication at any time, especially in the mental health field (Albrecht et al., 2021). Looking at practical applications of AI in mental health — they may offer solutions for in-between therapy for someone who is already in therapy. AI-driven apps and wearable sensors can collect, track and monitor behavior as well as bodily functioning that can be used to individualize treatment plans and support the communication flow between patients and health professionals (Bartels et al., 2019). That information is particularly important for patients who are suicidal or in mental distress because research has proven that general check-ins with these types of patients are essential to keep them safe (Oaklander, 2019).
Safe Space
Generally, when people feel ashamed or insecure, it can be hard to reach out to a human being. Asking for help is not easy. For some individuals, it may be easier to reach out to a bot instead, knowing it is anonymous and they won’t be judged (Asar, 2021; Oracle, 2020). It has been found that when coming into contact with a virtual therapist, people were more willing to express their feelings (Rucker, 2020).
Data processing
Great potential lies in the fact that AI can gather a lot of data and analyze data sets quickly and efficiently. Data Mining might be a possible field within data processing. It integrates automatic learning algorithms to determine, filter, and identify useful information and knowledge of big databases. Based on research done in this field, AI technology is indeed able to detect and predict mental health issues (Alonso et al., 2018; Wang et al., 2019). Another data assessment technology which also has been explored is Ecological Momentary Assessment (EMA). EMA is a data capture technique that focuses on data sampling of feelings, behaviors, or thoughts as close to real-time as possible (Moore et al., 2016). Reviews suggest that this data technique might be helpful in the prediction, prevention, and treatment of patients (Bell et al., 2017; Bos et al., 2015). For both data processing types the evidence is currently limited and more research is needed.
Speech Pattern Recognition and Diagnosis
Artificial intelligence can also be used to monitor speech behavior. Researchers discovered differences in the speech pattern of the different types of patients. Depressed patients tend to talk in a more monotonous and quiet voice with breaks in between, while anxious people have been found to speak faster (Williams, 2022). Current technologies are actually already able to use machine learning to identify vocal patterns to detect indications of mental health issues based on different voice samples. Some solutions are supposedly able to detect mental health issues with over 80% clinical accuracy and evaluate current stress levels based on short audio clips (Kesari, 2021; Williams, 2022). Researchers see a potential within speech processing in the assisting of mental health care, but there are some challenges within the technology, such as robustness, generalization, and ethical issues (Flanagan et al., 2021; Low et al., 2020).
Ethical AI
Is there such a thing as ethical AI, and what does that even mean? Where is my data going, and what is the AI doing with my data? Bluntly said, the more data algorithms process, the more accurate they become — in simple terms by providing more information about yourself, the algorithm learns and becomes better. However, there are limitations to AI solutions and big discussions around privacy and moral issues. Due to the data implemented in the technology real risks around privacy, ineffective care, and worsening disparities within the general implementation of AI in mental health care exist. Thus, it is crucial to think carefully about the design of machine learning algorithms to avoid the reproduction of human prejudices and stigmas and to consider the privacy aspects of data storage, usage, and processing.
All in all, to return to the question of the (dis)advantages of AI solutions — when looking at AI bots, in particular, they offer 24-hour availability, anywhere without time limitations. Ideally, they make you feel safe by operating as a safe space that is free of prejudices and stigma. Hence, AI can be a promising source of support for people facing geographic, economic, or social obstacles. Moreover, AI solutions do not aim to replace humans but instead offer advantages that are out of human capabilities at times — Why should they be competitors? Comparing AI with human therapists is a little bit like comparing pears and apples. At its current state, AI won’t replace human therapists, but instead it can offer something that is missing in mental health systems today.
Clare is able to provide a judgment-free and anonymous safe space, which is available anytime and anywhere 24/7 for its users. Based on your shared information on your current well-being and concerns, through one call or text message, Clare is able to match you with a self-reflective exercise or grounding technique with a focus on trying to help you feel better about yourself.
Mental health is a big challenge for today’s society, and the use of artificial intelligence can help to overcome some of its barriers; making mental health care more accessible, affordable, and responsive.
Sources:
Albrecht, T., Rausch, T. M., & Derra, N. D. (2021). Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting. Journal of Business Research, 123, 267–278. https://doi.org/10.1016/j.jbusres.2020.09.033
Alonso, S. G., de la Torre-Díez, I., Hamrioui, S., López-Coronado, M., Barreno, D. C., Nozaleda, L. M., & Franco, M. (2018). Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. Journal of Medical Systems, 42(9), 161. https://doi.org/10.1007/s10916-018-1018-2
Asar, A. (2021, April 2). Opening Up To A Robot? How Mental Health Tech Can Help Patients. Forbes. https://www.forbes.com/sites/forbestechcouncil/2021/04/02/opening-up-to-a-robot-how-mental-health-tech-can-help-patients/?sh=6f9aa0bd1722
Bartels, S. L., van Knippenberg, R. J. M., Dassen, F. C. M., Asaba, E., Patomella, A.-H., Malinowsky, C., Verhey, F. R. J., & de Vugt, M. E. (2019). A narrative synthesis systematic review of digital self-monitoring interventions for middle-aged and older adults. Internet Interventions, 18, 100283. https://doi.org/10.1016/j.invent.2019.100283
Bell, I. H., Lim, M. H., Rossell, S. L., & Thomas, N. (2017). Ecological Momentary Assessment and Intervention in the Treatment of Psychotic Disorders: A Systematic Review. Psychiatric Services (Washington, D.C.), 68(11), 1172–1181. https://doi.org/10.1176/appi.ps.201600523
Bendig E, Erb B, Schulze-Thuesing L, Baumeister H: The Next Generation: Chatbots in
Clinical Psychology and Psychotherapy to Foster Mental Health — A Scoping Review. Verhaltenstherapie 2019. doi: 10.1159/000501812.
Bos, F. M., Schoevers, R. A., & aan het Rot, M. (2015). Experience sampling and ecological momentary assessment studies in psychopharmacology: A systematic review. European Neuropsychopharmacology, 25(11), 1853–1864. https://doi.org/10.1016/j.euroneuro.2015.08.008
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811–817. https://doi.org/10.1017/S1351324916000243
Ducharme, J. (2019, November 20). Artificial Intelligence Could Be a Solution to America’s Mental Health Crisis. Time. https://time.com/5727535/artificial-intelligence-psychiatry/
Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health 2017 Jun 06;4(2):e19 [FREE Full text] [CrossRef] [Medline].
Flanagan, O., Chan, A., Roop, P., & Sundram, F. (2021). Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review. JMIR MHealth and UHealth, 9(9), e24352. https://doi.org/10.2196/24352
Frazier P, Richards D, Mooney J, Hofmann SG, Beidel D, Palmieri PA. Internet-delivered treatment for depression, anxiety, and stress in university students: a patient preference trial. iproc 2016 Dec 08;2(1):e5 [FREE Full text] [CrossRef].
Garg, P., & Glick, S. (2018, October 22). AI’s Potential to Diagnose and Treat Mental Illness. Harvard Business Review. https://hbr.org/2018/10/ais-potential-to-diagnose-and-treat-mental-illness
Isaksen, K. J., & Roper, S. (2012). The Commodification of Self-Esteem: Branding and British Teenagers: ISAKSEN AND ROPER. Psychology & Marketing, 29(3), 117–135. https://doi.org/10.1002/mar.20509
Kesari, G. (n.d.). AI Can Now Detect Depression From Your Voice, And It’s Twice As Accurate As Human Practitioners. Forbes. Retrieved January 11, 2023, from https://www.forbes.com/sites/ganeskesari/2021/05/24/ai-can-now-detect-depression-from-just-your-voice/
Low, D. M., Bentley, K. H., & Ghosh, S. S. (2020). Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investigative Otolaryngology, 5(1), 96–116. https://doi.org/10.1002/lio2.354
Moore, R. C., Depp, C. A., Wetherell, J. L., & Lenze, E. J. (2016). Ecological momentary assessment versus standard assessment instruments for measuring mindfulness, depressed mood, and anxiety among older adults. Journal of Psychiatric Research, 75, 116–123.
Oaklander, M. (2019, October 24). Suicide Is Preventable. Hospitals and Doctors Are Finally Catching Up. Time. https://time.com/5709368/how-to-solve-suicide/
Oracle. (2020). Global Study: 82% of People Believe Robots Can Support Their Mental Health Better Than Humans. https://www.oracle.com/news/announcement/ai-at-work-100720/
Richards D, Richardson T. Computer-based psychological treatments for depression: A systematic review and meta-analysis. Clin Psychol Rev. 2012; 32: 329–342. https://doi.org/10.1016/j.cpr.2012.02. 004 PMID: 22466510.
Rucker, M. (2020, September 17). Using AI for Mental Health. Verywell Health. https://www.verywellhealth.com/using-artificial-intelligence-for-mental-health-4144239
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44–56.
Twomey, C., O’Reilly, G., & Meyer, B. (2017). Effectiveness of an individually-tailored computerised CBT programme (Deprexis) for depression: a meta-analysis. Psychiatry research, 256, 371–377.
Wang, X., Hu, J., Chen, Q., Zhang, P., Wu, L., Li, J., Liu, B., Xiao, K., Liang, S., Huang, L., Hou, H., & Yang, J. (2019). Synergic degradation of 2,4,6-trichlorophenol in microbial fuel cells with intimately coupled photocatalytic-electrogenic anode. Water Research, 156, 125–135. https://doi.org/10.1016/j.watres.2019.03.001
WHO. (2022). World mental health report: Transforming mental health for all (p. 296). World Health Organization. https://www.who.int/publications/i/item/9789240049338
Williams, I. K. (2022, April 5). Can A.I.-Driven Voice Analysis Help Identify Mental Disorders? The New York Times. https://www.nytimes.com/2022/04/05/technology/ai-voice-analysis-mental-health.html
