Medicine:Artificial intelligence in mental health

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Artificial Intelligence (AI) in mental health refers to the use of advanced computational technologies and algorithms to enhance the understanding, diagnosis, and treatment of mental health disorders.[1]

Artificial intelligence
Major goals
Approaches
Philosophy
History
Technology
Glossary

Background

Mental illness are the first in global burden of diseases,[2] accounting for about 1 billion people affected by mental health and addiction disorders in 2016, constituting about 6% of the world population at the time, with a relatively proportional representation between men and women.[3] This amounts to about 162.5 disability-adjusted life years (DALY) lost when adding all the years that the patients suffering from these illnesses have lost due to their diseases' morbidities, mortalities, and quality of life.[3] In recent years and due to COVID-19 mental health illnesses have increased, with a marked increase in loneliness, suicidality, and substance use, just to name a few.[2] The problem is then made worse due to the shortage in healthcare providers and licensed psychiatrists and therapists worldwide.

Use of AI technologies may help reduce this shortage by making mental healthcare professionals more efficient and effective in their work.[2] The AI market in healthcare is estimated to grow from a $5 billion industry in 2020 to $45 billion in 2026.[2]

Types of AI in mental health

As of 2020, there was no Food and Drug Administration (FDA) approval for AI in the field of Psychiatry.[4] This may be due to the large and complex dataset which is required to train any AI model in psychiatric decision making or analysis.[2][5] The biggest two domains of AI that are currently widely available for multiple applications are Machine learning (ML) and Natural Language Processing (NLP).

Machine Learning

Machine learning is a way for a computer to learn from large datasets presented to it, with few assumptions to begin with. It requires structured databases, unlike scientific research which begins with a hypothesis, ML begins by looking at the data and finding its own hypothesis based on the patterns it detects.[2] It then creates algorithms to be able to predict new information, based on the created algorithm and pattern it was able to generate from the original dataset.[2] This model of AI is data driven, it requires a huge amount of structured data, an obstacle in the field of psychiatry which relies mostly on complex DSM-5 definitions for diseases, with a lot of its patient encounters being based on interview and story telling on the part of the patient.[2] It is for those reasons that some researchers adopted a different method to creating ML models to be used in psychiatry based on trained models in different fields, a process termed transfer learning.[2]

Transfer learning was used by researchers to develop a modified algorithm to detect alcoholism vs. non-alcoholism, and on another occasion the same method was used to detect the signs of post-traumatic stress disorder.[6][7]

Natural Language Processing

One of the obstacles for AI is finding or creating an organized dataset to train and develop a useful algorithm. NLP can be used to create such a dataset. NLP is a technique that takes in semantic, lexical, speech recognition, and optical character recognition to take in unstructured data and turn it into a structured one.[2] This is crucial because many of the diagnoses and DSM-5 mental health disorders are diagnosed by speech and doctor patient interview, utilising the clinician's skill for behavioural pattern recognition and translating into medically relevant information to be documented and used for diagnoses. NLP can be used to extract, order and structure data on patients from their everyday interaction and not just during a clinical visit, with this comes many ethical issues.[8][2]

Applications

Diagnosis

AI with the use of NLP and ML can be used to diagnose individuals with mental health disorders.[2] It can be used to differentiate closely similar disorders based on their initial presentation to inform timely treatment before disease progression. For example, it may be able to differentiate unipolar from bipolar depression from imaging and medical scans or differentiating between different forms of dementia.[2] AI has the potential to also identify novel diseases that were overlooked due to the heterogeneity of presentation of a single disorder,[2] this means that while many people get diagnosed with depression, that depression may take on different forms and be enacted in different behaviours - AI may parse through the variability of human expression and potentially identify different types of depression or maybe a completely different form of disease that may have been being misidentified in medicine.[citation needed]

Prognosis

AI can be used to create accurate predictions for disease progression once diagnosed.[2] AI algorithms do not have to follow the current assumptions on diseases and can formulate their own hypotheses and tests to validate new algorithms to predict disease progression and quality of life.[2] In fact, some studies have used neuroimaging, electronic health records, genetic, and speech data to predict how depression would present in patients, their risk for suicidality or substance abuse, or functional outcomes.[2]

Treatment

In psychiatry, in many cases multiple drugs are trialed with the patients until the correct combination or regimen is reached to effectively treat their ailment - AI can be used to predict treatment response based on observed data collected from the various sources that it would theoretically have at it's disposal.[2] This would essentially bypass all the time, effort, resources needed and burden placed on both patients and clinicians.[2]

Benefits

AI in mental health offers several benefits, such as:

  • Improving the accuracy of diagnosis: AI-based systems can analyze data from various sources, such as brain imaging and genetic tests, to identify biomarkers of mental health conditions and improve the accuracy of diagnosis.[9]
  • Personalized treatment: AI-based systems can analyze data from EHRs, brain imaging, and genetic tests to identify the most effective treatment for specific individuals.[9]
  • Improving access to mental health care: AI-based systems can be used to deliver mental health interventions, such as cognitive behavioral therapy, in virtual environments, which can improve access to mental health care in areas where access is limited.[9]

Criticism

AI in mental health is still an emerging field and there are still some concerns and criticisms about the use of AI in this area, such as:

  • Lack of data: There is a lack of data available to train AI systems, which limits their ability to identify patterns in mental health conditions and predict outcomes.[10]
  • Bias: AI systems can be biased if the data used to train them is biased. This can lead to inaccurate predictions and unfair treatment of certain groups of people.[11]
  • Privacy: The use of AI in mental health raises concerns about privacy, as large amounts of personal data are collected and analyzed.[12]

Conclusion

Mental health conditions such as depression, anxiety, and post-traumatic stress disorder (PTSD) are major public health concerns, and they affect a large proportion of the population. Traditional methods of mental health care, such as psychotherapy and medication, have been shown to be effective, but they also have limitations.[13] For example, access to mental health care can be limited in certain areas, and it can be difficult to accurately diagnose and treat mental health conditions. AI technologies have the potential to improve the diagnosis and treatment of mental health conditions by providing new insights and identifying patterns that may not be visible to human experts.[14]

See also

References

  1. ↑ Mazza, Gabriella (2022-08-29). "AI and the Future of Mental Health" (in en-US). https://www.cengn.ca/information-centre/innovation/artificial-intelligence-ai-and-the-future-of-mental-health/. 
  2. ↑ 2.00 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 Lee, Ellen E.; Torous, John; De Choudhury, Munmun; Depp, Colin A.; Graham, Sarah A.; Kim, Ho-Cheol; Paulus, Martin P.; Krystal, John H. et al. (September 2021). "Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom" (in en). Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 6 (9): 856–864. doi:10.1016/j.bpsc.2021.02.001. PMID 33571718. 
  3. ↑ 3.0 3.1 Rehm, Jürgen; Shield, Kevin D. (2019-02-07). "Global Burden of Disease and the Impact of Mental and Addictive Disorders" (in en). Current Psychiatry Reports 21 (2): 10. doi:10.1007/s11920-019-0997-0. ISSN 1535-1645. PMID 30729322. https://doi.org/10.1007/s11920-019-0997-0. 
  4. ↑ Benjamens, Stan; Dhunnoo, Pranavsingh; Meskó, Bertalan (2020-09-11). "The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database" (in en). npj Digital Medicine 3 (1): 118. doi:10.1038/s41746-020-00324-0. ISSN 2398-6352. PMID 32984550. 
  5. ↑ Gabbard, Glen O.; Crisp-Han, Holly (February 2017). "The Early Career Psychiatrist and the Psychotherapeutic Identity" (in en). Academic Psychiatry 41 (1): 30–34. doi:10.1007/s40596-016-0627-7. ISSN 1042-9670. PMID 27882522. http://link.springer.com/10.1007/s40596-016-0627-7. 
  6. ↑ Wang, Shui-Hua; Xie, Shipeng; Chen, Xianqing; Guttery, David S.; Tang, Chaosheng; Sun, Junding; Zhang, Yu-Dong (2019-04-11). "Alcoholism Identification Based on an AlexNet Transfer Learning Model". Frontiers in Psychiatry 10: 205. doi:10.3389/fpsyt.2019.00205. ISSN 1664-0640. PMID 31031657. 
  7. ↑ Banerjee, Debrup; Islam, Kazi; Xue, Keyi; Mei, Gang; Xiao, Lemin; Zhang, Guangfan; Xu, Roger; Lei, Cai et al. (2019-09-01). "A deep transfer learning approach for improved post-traumatic stress disorder diagnosis" (in en). Knowledge and Information Systems 60 (3): 1693–1724. doi:10.1007/s10115-019-01337-2. ISSN 0219-3116. https://doi.org/10.1007/s10115-019-01337-2. 
  8. ↑ Le Glaz, Aziliz; Haralambous, Yannis; Kim-Dufor, Deok-Hee; Lenca, Philippe; Billot, Romain; Ryan, Taylor C; Marsh, Jonathan; DeVylder, Jordan et al. (2021-05-04). "Machine Learning and Natural Language Processing in Mental Health: Systematic Review" (in en). Journal of Medical Internet Research 23 (5): e15708. doi:10.2196/15708. ISSN 1438-8871. PMID 33944788. 
  9. ↑ 9.0 9.1 9.2 "AI in Mental Health - Examples, Benefits & Trends" (in en-US). 2022-12-13. https://itrexgroup.com/blog/ai-mental-health-examples-trends/. 
  10. ↑ Ćosić, Krešimir; Popović, Siniša; Šarlija, Marko; Kesedžić, Ivan; Jovanovic, Tanja (June 2020). "Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers". Croatian Medical Journal 61 (3): 279–288. doi:10.3325/cmj.2020.61.279. ISSN 0353-9504. PMID 32643346. 
  11. ↑ Nilsen, Per; Svedberg, Petra; Nygren, Jens; Frideros, Micael; Johansson, Jan; Schueller, Stephen (January 2022). "Accelerating the impact of artificial intelligence in mental healthcare through implementation science" (in en). Implementation Research and Practice 3: 263348952211120. doi:10.1177/26334895221112033. ISSN 2633-4895. PMID 37091110. 
  12. ↑ Royer, Alexandrine (2021-10-14). "The wellness industry's risky embrace of AI-driven mental health care" (in en-US). https://www.brookings.edu/techstream/the-wellness-industrys-risky-embrace-of-ai-driven-mental-health-care/. 
  13. ↑ "4 ways artificial intelligence is improving mental health therapy" (in en). 22 December 2021. https://www.weforum.org/agenda/2021/12/ai-mental-health-cbt-therapy/. 
  14. ↑ HealthITSecurity (2021-04-23). "What Role Could Artificial Intelligence Play in Mental Healthcare?" (in en-US). https://healthitanalytics.com/features/what-role-could-artificial-intelligence-play-in-mental-healthcare. 



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