Submit Manuscript

Journal of Artificial Intelligence

Year: 2022 | Volume: 15 | Issue: 1 | Page No.: 1-8
Review Article

Role of Artificial Intelligence in Mental Wellbeing: Opportunities and Challenges

Bavly Samy Helmy Hanna
Canadian Higher Institute of Engineering, Egypt
LiveDNA: 61.36408
Andrew Samy Helmy Hanna
Faculty of Medicine, Ain Shams University, Egypt
COVID-19 has exposed the public to enormous mental disorders especially with social distance and limited resources for mental health. The surge of AI does not mean only a turning point of diagnosis and treatment of mental disorders, but also, the way we define mental health issues. AI offers many potential opportunities to be implemented in the field of mental health assessment and treatment. AI enhances mental wellbeing through internet-based cognitive behavioural therapy chatbots, intelligent virtual worlds and artificial companions, augmented reality applications, therapeutic computer games and electronic medical records. However, these opportunities come with several challenges of users’ privacy, data security, bias, consent, governance and regulation. With the availability of several AI options, psychologists and psychiatrists must pick a suitable tool based on their needs, resources available and implementation practicality. There is a need to develop indigenous proprietary technology for mental health, test it and validate it. Harmony between traditional and technologically based treatment must be achieved overtime.
PDF Fulltext XML References Citation

Leave a Comment

Your email address will not be published. Required fields are marked *

Article Trend

Total views 11719

References

  1. Jiang, F., Y. Jiang, H. Zhi, Y. Dong and H. Li et al., 2017. Artificial intelligence in healthcare: Past, present and future. Stroke Vascular Neurol., 2: 230-243.
    CrossRefDirect Link
  2. Miller, D.D. and E.W. Brown, 2018. Artificial intelligence in medical practice: The question to the answer? Am. J. Med., 131: 129-133.
    CrossRefDirect Link
  3. Gabbard, G.O. and H. Crisp-Han, 2017. The early career psychiatrist and the psychotherapeutic identity. Acad. Psychiatry, 41: 30-34.
    CrossRefDirect Link
  4. Janssen, R.J., J. Mourão-Miranda and H.G. Schnack, 2018. Making individual prognoses in psychiatry using neuroimaging and machine learning. Biol. Psychiatry: Cognitive Neurosci. Neuroimaging, 3: 798-808.
    CrossRefDirect Link
  5. Luxton, D.D., 2014. Artificial intelligence in psychological practice: current and future applications and implications. Professional Psychol.: Res. Pract., 45: 332-339.
    CrossRefDirect Link
  6. Mohr, D.C., M. Zhang and S.M. Schueller, 2017. Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Rev. Clin. Psychol., 13: 23-47.
    CrossRefDirect Link
  7. Shatte, A.B.R., D.M. Hutchinson and S.J. Teague, 2019. Machine learning in mental health: A scoping review of methods and applications. Psychol. Med., 49: 1426-1448.
    CrossRefDirect Link
  8. Iniesta, R., D. Stahl and P. McGuffin, 2016. Machine learning, statistical learning and the future of biological research in psychiatry. Psychol. Med., 46: 2455-2465.
    CrossRefDirect Link
  9. Bzdok, D. and A. Meyer-Lindenberg, 2018. Machine learning for precision psychiatry: Opportunities and challenges. Biol. Psychiatry: Cognitive Neurosci. Neuroimaging, 3: 223-230.
    CrossRefDirect Link
  10. Jeste, D.V., D. Glorioso, E.E. Lee, R. Daly and S. Graham et al., 2019. Study of independent living residents of a continuing care senior housing community: Sociodemographic and clinical associations of cognitive, physical, and mental health. Am. J. Geriatric Psychiatry, 27: 895-907.
    CrossRefDirect Link
  11. Zilcha-Mano, S., S.P. Roose, P.J. Brown and B.R. Rutherford, 2018. A machine learning approach to identifying placebo responders in late-life depression trials. Am. J. Geriatric Psychiatry, 26: 669-677.
    CrossRefDirect Link
  12. Chekroud, A.M., R.J. Zotti, Z. Shehzad, R. Gueorguieva and M.K. Johnson et al., 2016. Cross-trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatry, 3: 243-250.
    CrossRefDirect Link
  13. Chekroud, A.M., R. Gueorguieva, H.M. Krumholz, M.H. Trivedi, J.H. Krystal and G. McCarthy, 2017. Reevaluating the efficacy and predictability of antidepressant treatments. JAMA Psychiatry, 74: 370-378.
    CrossRefDirect Link
  14. Fitzpatrick, K.K., A. Darcy and M. Vierhile, 2017. 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 Mental Health, Vol. 4.
    CrossRefDirect Link
  15. Hamamura, T., S. Suganuma, M. Ueda, J. Mearns and H. Shimoyama, 2018. Standalone effects of a cognitive behavioral intervention using a mobile phone app on psychological distress and alcohol consumption among Japanese workers: Pilot nonrandomized controlled trial. JMIR Mental Health, Vol. 5.
    CrossRefDirect Link
  16. Abdul-Kader, S.A. and J.C. Woods, 2015. Survey on chatbot design techniques in speech conversation systems. Int. J. Adv. Comput. Sci. Appl., Vol. 6, No. 7.
    CrossRefDirect Link
  17. Chowdhury, G.G., 2003. Natural language processing. Ann. Rev. Inf. Sci. Technol., 37: 51-89.
    CrossRefDirect Link
  18. Glaz, A.L., Y. Haralambous, D.H. Kim-Dufor, P. Lenca and R. Billot et al., 2021. Machine learning and natural language processing in mental health: systematic review. J. Med. Internet Res., Vol. 23.
    CrossRefDirect Link
  19. Thieme, A., D. Belgrave and G. Doherty, 2020. Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems ACM Trans. Comput.-Hum. Interact., Vol. 27.
    CrossRefDirect Link
  20. Vaidyam, A.N., H. Wisniewski, J.D. Halamka, M.S. Kashavan and J.B. Torous, 2019. Chatbots and conversational agents in mental health: A review of the psychiatric landscape. Can. J. Psychiatry, 64: 456-464.
    CrossRefDirect Link
  21. Skowron, M., S. Rank, D. Garcia and J.A. Hołyst, 2017. Zooming in: Studying Collective Emotions with Interactive Affective Systems. In: Cyberemotions, Holyst, J.A. (Ed.)., Springer, Cham, pp: 279-304.
    CrossRefDirect Link
  22. Cominelli, L., D. Mazzei and D.E. De Rossi, 2018. Social emotional artificial intelligence based on damasio`s theory of mind. Front. Rob. AI, Vol. 5.
    CrossRefDirect Link
  23. Anselma, L. and A. Mazzei, 2020. Building a persuasive virtual dietitian. Informatics, Vol. 7.
    CrossRefDirect Link
  24. Wollny, S., J. Schneider, D.D. Mitri, J. Weidlich, M. Rittberger and H. Drachsler, 2021. Are we there yet? - A systematic literature review on chatbots in education. Front. Artif. Intell., Vol. 4.
    CrossRefDirect Link
  25. Smutny, P. and P. Schreiberova, 2020. Chatbots for learning: A review of educational chatbots for the facebook messenger. Comput. Educ., Vol. 151.
    CrossRefDirect Link
  26. Rizzo, A., T.D. Parsons, B. Lange, P. Kenny and J.G. Buckwalter et al., 2011. Virtual reality goes to war: A brief review of the future of military behavioral healthcare. J. Clin. Psychol. Med. Settings, 18: 176-187.
    CrossRefDirect Link
  27. Gorrindo, T. and J.E. Groves, 2009. Computer simulation and virtual reality in the diagnosis and treatment of psychiatric disorders. Acad. Psychiatry, 33: 413-417.
    CrossRefDirect Link
  28. Krijn, M., P.M.G. Emmelkamp, R.P. Olafsson and R. Biemond, 2004. Virtual reality exposure therapy of anxiety disorders: A review. Clin. Psychol. Rev., 24: 259-281.
    CrossRefDirect Link
  29. Reger, G.M., K.M. Holloway, C. Candy, B.O. Rothbaum, J. Difede, A.A. Rizzo and G.A. Gahm, 2011. Effectiveness of virtual reality exposure therapy for active duty soldiers in a military mental health clinic. J. Traumatic Stress, 24: 93-96.
    CrossRefDirect Link
  30. Freeman, D., S. Reeve, A. Robinson, A. Ehlers, D. Clark, B. Spanlang and M. Slater, 2017. Virtual reality in the assessment, understanding, and treatment of mental health disorders. Psychol. Med., 47: 2393-2400.
    CrossRefDirect Link
  31. Shibata, T. and K. Wada, 2011. Robot therapy: A new approach for mental healthcare of the elderly – a mini-review. Gerontology, 57: 378-386.
    CrossRefDirect Link
  32. Rohrbach, N., P. Gulde, A.R. Armstrong, L. Hartig and A. Abdelrazeq et al., 2019. An augmented reality approach for ADL support in Alzheimer`s disease: A crossover trial. J. Neuroeng. Rehabil., Vol. 16.
    CrossRefDirect Link
  33. Lingley, A.R., M. Ali, Y. Liao, R. Mirjalili and M. Klonner et al., 2011. A single-pixel wireless contact lens display. J. Micromech. Microeng., Vol. 21.
    CrossRefDirect Link
  34. Kim, J., M. Kim, M.S. Lee, K. Kim and S. Ji et al., 2017. Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics. Nat. Commun., Vol. 8.
    CrossRefDirect Link
  35. Coyle, D., M. Matthews, J. Sharry, A. Nisbet and G. Doherty, 2005. Personal investigator: A therapeutic 3D game for adolecscent psychotherapy. Interact. Technol. Smart Educ., 2: 73-88.
    CrossRefDirect Link
  36. Fujita, H. and I.C. Wu, 2012. A special issue on artificial intelligence in computer games: AICG. Knowledge-Based Syst., 34: 1-2.
    CrossRefDirect Link
  37. von der Heiden, J.M., B. Braun, K.W. Müller and B. Egloff, 2019. The association between video gaming and psychological functioning. Front. Psychol., Vol. 10.
    CrossRefDirect Link
  38. Fleming, T.M., L. Bavin, K. Stasiak, E. Hermansson-Webb and S.N. Merry et al., 2017. Serious games and gamification for mental health: Current status and promising directions. Front. Psychiatry, Vol. 7.
    CrossRefDirect Link
  39. Steinfeld, B.I. and J.A. Keyes, 2011. Electronic medical records in a multidisciplinary health care setting: A clinical perspective. Professional Psychol. Res. Pract., 42: 426-432.
    CrossRefDirect Link
  40. Jarrett, M.P., 2017. Cybersecurity—a serious patient care concern. JAMA, Vol. 318.
    CrossRefDirect Link
  41. Wolfe, L., M.S. Chisolm and F. Bohsali, 2018. Clinically excellent use of the electronic health record: review. JMIR Hum. Factors, Vol. 5.
    CrossRefDirect Link
  42. Magruder, J.A., B.S. Adams, P. Pohto and T.L. Smith, 2018. Clinicians’ experiences of transition to electronic health records. J. Coll. Couns., 21: 210-223.
    CrossRefDirect Link
  43. Shenoy, A. and J.M. Appel, 2017. Safeguarding confidentiality in electronic health records. Cambridge Q. Healthcare Ethics, 26: 337-341.
    CrossRefDirect Link
  44. Yüksel, B., A. Küpçü and Ö. Özkasap, 2017. Research issues for privacy and security of electronic health services. Future Gener. Comput. Syst., 68: 1-13.
    CrossRefDirect Link
  45. Holmes, C.M. and C.A. Reid, 2018. Ethics in telerehabilitation: Looking ahead. J. Appl. Rehabil. Couns., 49: 14-23.
    CrossRefDirect Link
  46. Price, W.N., S. Gerke and I.G. Cohen, 2019. Potential liability for physicians using artificial intelligence. JAMA, 322: 1765-1766.
    CrossRefDirect Link
  47. Librenza-Garcia, D., 2019. Ethics in the Era of Big Data. In: Personalized Psychiatry: Big Data Analytics in Mental Health, Cavalcante, P.I., M. Benson and K. Flavio (Eds.)., Springer, Switzerland, pp: 161-172.
    CrossRefDirect Link
  48. Lawrie, S.M., S. Fletcher-Watson, H.C. Whalley and A.M. McIntosh, 2019. Predicting major mental illness: Ethical and practical considerations. BJPsych Open, Vol. 5.
    CrossRefDirect Link
  49. O`Loughlin, K., M. Neary, E.C. Adkins and S.M. Schueller, 2019. Reviewing the data security and privacy policies of mobile apps for depression. Internet Interventions, 15: 110-115.
    CrossRefDirect Link
  50. Miotto, R., F. Wang, S. Wang, X. Jiang and J.T. Dudley, 2017. Deep learning for healthcare: Review, opportunities and challenges. Briefings Bioinf., 19: 1236-1246.
    CrossRefDirect Link
  51. Park, S.H. and K. Han, 2018. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 286: 800-809.
    CrossRefDirect Link
  52. Jordan, M.I. and T.M. Mitchell, 2015. Machine learning: Trends, perspectives and prospects. Science, 349: 255-260.
    CrossRefDirect Link
  53. Lee, E.E., C. Depp, B.W. Palmer, D. Glorioso and R. Daly et al., 2018. High prevalence and adverse health effects of loneliness in community-dwelling adults across the lifespan: Role of wisdom as a protective factor. Int. Psychogeriatrics, 31: 1447-1462.
    CrossRefDirect Link
  54. Jeste, D.V., 2018. Positive psychiatry comes of age. Int. Psychogeriatrics, 30: 1735-1738.
    CrossRefDirect Link
  55. Lemaitre, G, F. Nogueira and C.K. Aridas, 2017. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn Res., 18: 559-563.
    Direct Link
  56. Kessler, R.C., I. Hwang, C.A. Hoffmire, J.F. McCarthy and M.V. Petukhova et al., 2017. Developing a practical suicide risk prediction model for targeting high‐risk patients in the veterans health administration. Int. J. Methods Psychiatric Res., Vol. 26.
    CrossRefDirect Link
  57. Choi, S.B., W. Lee, J.H. Yoon, J.U. Won and D.W. Kim, 2018. Ten-year prediction of suicide death using cox regression and machine learning in a nationwide retrospective cohort study in South Korea. J. Affective Disord., 231: 8-14.
    CrossRefDirect Link
  58. Šimundić, A.M., 2009. Measures of diagnostic accuracy: Basic definition. EJIFCC, 19: 203-211.
    Direct Link
  59. Wahle, F., T. Kowatsch, E. Fleisch, M. Rufer and S. Weidt, 2016. Mobile sensing and support for people with depression: A pilot trial in the wild. JMIR mHealth uHealth, Vol. 4. 4: e111-0.
    CrossRefDirect Link
  60. Glenn, T. and S. Monteith, 2014. Privacy in the digital world: Medical and health data outside of hipaa protections. Curr. Psychiatry Rep., Vol. 16.
    CrossRefDirect Link
  61. Shen, N., L. Sequeira, M.P. Silver, A. Carter-Langford, J. Strauss and D. Wiljer, 2019. Patient privacy perspectives on health information exchange in a mental health context: qualitative study. JMIR Mental Health, Vol. 6.
    CrossRefDirect Link

Keywords