Current Trends in Signal Processing Original Research

Digital Psychiatry: A Narrative Review on AI Positive Role in Mental Health

  1. Sohail Verma Department of Management, Guru Kashi University
  2. Dr Pretty Bhalla Department of Management, Lovely Professional University
  3. Simran Monga ³Department of Management, Guru Kashi University

Abstract

Artificial Intelligence has rapidly evolved into a formidable instrument within the domain of mental healthcare, fundamentally altering the way we understand awareness, diagnosis, intervention and emotional regulation. This narrative review explores AI’s potential to foster positive mental health through tools such as natural language processing, machine learning, deep learning and computer vision. These technologies promise earlier detection of mental disorders, customized treatment plans and responsive emotional support. Yet, alongside these possibilities arise profound concerns issues of data integrity, algorithmic bias, ethical ambiguity and cultural blindness remain unresolved. At the core of this discussion lies a fundamental principle: AI must serve as a collaborator, not a replacement, for human judgment. The review emphasizes the indispensable role of transparency, inclusivity in data training and ethical stewardship in deploying AI responsibly. Going forward, research must prioritize the refinement of interpretability, the elimination of systemic bias and the integration of diverse cultural frameworks to truly realize AI’s potential in advancing global mental health care.

Keywords

References (71)

  1. G. Solomonoff, "Ray Solomonoff and the Dartmouth summer research project in artificial intelligence (1956)," unpublished manuscript, 2017.
  2. J. Kaplan, Artificial Intelligence: What Everyone Needs to Know. New York, NY: Oxford University Press, 2016.
  3. J. McCarthy, "Making robots conscious of their mental states," presented at Machine Intelligence 15 Workshop, Oxford University, UK, 1995.
  4. W. Ertel, N. Black and F. Mast, Introduction to Artificial Intelligence. Cham, Switzerland: Springer, 2017.
  5. P. McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. Boca Raton, FL: CRC Press, 2004.
  6. P. Wang, "On defining artificial intelligence," J. Artif. Gen. Intell., vol. 10, no. 2, pp. 1-37, 2019, doi:10.2478/jagi-2019- 0002.
  7. R. Anyoha, "The history of artificial intelligence," Science in the News, Harvard University, 2017. [Online]. Available: https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence. [Accessed: 24-Sep-2023].
  8. J. Moor, The Turing Test: The Elusive Standard of Artificial Intelligence, vol. 30. Norwell, MA: Springer Science & Business Media, 2003.
  9. J. McCarthy, "Programs with common sense," Mechanization of Thought Processes, vol. I. London: Her Majesty's Stationery Office, UK, 1959.
  10. Newell A, Shaw JC, Simon HA. Elements of a theory of human problem solving. Psychological Review. 1958;65(3):151-166. doi:10.1037/h0048495
  11. E. M. Feigenbaum and M. C. Corduck, The Fifth Generation. New York: Addison-Wesley, 1983.
  12. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. London: Pearson Education Limited, 2016.
  13. LeCun B, Mautor T, Quessette F, Weisser MA. Bin packing with fragmentable items: Presentation and approximations. Theoretical Computer Science. 2015;602:50-59. doi:10.1016/j.tcs.2015.08.005
  14. I. Goodfellow, Y. Bengio and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.
  15. Kelly S, Kaye SA, Oviedo-Trespalacios O. A Multi-Industry Analysis of the Future Use of AI Chatbots. Human Behavior and Emerging Technologies. 2022;2022:1-14. doi:10.1155/2022/2552099
  16. Dirican C. The Impacts of Robotics, Artificial Intelligence On Business and Economics. Procedia - Social and Behavioral Sciences. 2015;195:564-573. doi:10.1016/j.sbspro.2015.06.134
  17. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. npj Digital Medicine. 2018;1(1). doi:10.1038/s41746-018-0029-1
  18. Mumali F. Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review. Computers & Industrial Engineering. 2022;165:107964. doi:10.1016/j.cie.2022.107964
  19. G. Litjens, B. B. Kausika, E. Worrell and W. van Sark, "Spatial analysis of residential combined photovoltaic and battery potential: Case study Utrecht, The Netherlands," in IEEE 44th Photovoltaic Specialist Conference (PVSC), 2017, pp. 3014-3019.
  20. T. H. Davenport, "Artificial intelligence for the real world," Harvard Bus. Rev., 2018. [Online]. Available: https://hbr.org/webinar/2018/02/artificial-intelligence-for-the-real-world. [Accessed: 02-Oct-2023].
  21. Özkiziltan D. Melanie Mitchell: Artificial intelligence—a guide for thinking humans. Genetic Programming and Evolvable Machines. 2022;23(4):581-582. doi:10.1007/s10710-022-09439-7
  22. D. Becker, "Possibilities to improve online mental health treatment: Recommendations for future research and developments," in Future Inf. Commun. Conf., Singapore: Springer, 2018.
  23. Chen B, Kwiatkowski R, Vondrick C, Lipson H. Fully body visual self-modeling of robot morphologies. Science Robotics. 2022;7(68). doi:10.1126/scirobotics.abn1944
  24. Accenture, "From AI compliance to competitive advantage," 2022. [Online]. Available: https://www.accenture.com/us- en/insights/artificialintelligence/ai-compliance-competitive-advantage. [Accessed: 21-Dec-2023].
  25. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports. 2019;21(11). doi:10.1007/s11920-019-1094-0
  26. D. Raphael-Rene and J. Name, "Artificial intelligence for competitive advantage: Grata," Grata Software, 2023. [Online]. Available: https://www.gratasoftware.com/artificial-intelligence-for-competitive-advantage-insights-for-business-leaders. [Accessed: 21-Dec-2023].
  27. Fabris F, Magalhães JPD, Freitas AA. A review of supervised machine learning applied to ageing research. Biogerontology. 2017;18(2):171-188. doi:10.1007/s10522-017-9683-y
  28. Bzdok D, Krzywinski M, Altman N. Machine learning: supervised methods. Nature Methods. 2018;15(1):5-6. doi:10.1038/nmeth.4551
  29. Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports. 2016;6(1). doi:10.1038/srep26094
  30. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. doi:10.1038/nature14539
  31. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics. 2017;19(6):1236-1246. doi:10.1093/bib/bbx044
  32. Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR. Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine. 2018;161:1-13. doi:10.1016/j.cmpb.2018.04.005
  33. Hirschberg J, Manning CD. Advances in natural language processing. Science. 2015;349(6245):261-266. doi:10.1126/science.aaa8685
  34. Gottesman O, Johansson F, Komorowski M, Faisal A, Sontag D, Doshi-Velez F, et al. Guidelines for reinforcement learning in healthcare. Nature Medicine. 2019;25(1):16-18. doi:10.1038/s41591-018-0310-5
  35. S. M. Ahmed, S. Lohit, K. C. Peng, M. J. Jones and A. K. Roy-Chowdhury, "Cross-modal knowledge transfer without task-relevant source data," Eur. Conf. Comput. Vis., Switzerland: Springer Nature, 2022, pp. 111-127.
  36. Mowery D, Smith H, Cheney T, Stoddard G, Coppersmith G, Bryan C, et al. Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study. Journal of Medical Internet Research. 2017;19(2):e48. doi:10.2196/jmir.6895
  37. F. Minerva and A. Giubilini, "Is AI the future of mental healthcare?" Topoi, vol. 42, pp. 1-9, 2023, doi:10.1007/s11245- 023-09932-3.
  38. Tutun S, Johnson ME, Ahmed A, Albizri A, Irgil S, Yesilkaya I, et al. An AI-based Decision Support System for Predicting Mental Health Disorders. Information Systems Frontiers. 2022;25(3):1261-1276. doi:10.1007/s10796-022-10282-5
  39. Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous JB. Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape. The Canadian Journal of Psychiatry. 2019;64(7):456-464. doi:10.1177/0706743719828977
  40. K. Denecke, A. Abd-Alrazaq and M. Househ, "Artificial intelligence for chatbots in mental health: Opportunities and challenges," in Multiple Perspectives on Artificial Intelligence in Healthcare: Opportunities and Challenges, M. Househ, E. Borycki and A. Kushniruk, Eds., Cham, Switzerland: Springer International Publishing, 2021, pp. 115-128.
  41. Chaudhary S, Kumaran SS, Kaloiya GS, Goyal V, Sagar R, Kalaivani M, et al. Domain specific cognitive impairment in Parkinson’s patients with mild cognitive impairment. Journal of Clinical Neuroscience. 2020;75:99-105. doi:10.1016/j.jocn.2020.03.015
  42. Javed AR, Saadia A, Mughal H, Gadekallu TR, Rizwan M, Maddikunta PKR, et al. Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognitive Computation. 2023;15(6):1767-1812. doi:10.1007/s12559-023-10153-4
  43. Zheng Z, Zheng P, Zou X. Peripheral Blood S100B Levels in Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. Journal of Autism and Developmental Disorders. 2020;51(8):2569-2577. doi:10.1007/s10803-020-04710-1
  44. Michel PP, Hirsch EC, Hunot S. Understanding Dopaminergic Cell Death Pathways in Parkinson Disease. Neuron. 2016;90(4):675-691. doi:10.1016/j.neuron.2016.03.038
  45. Klöppel S, for the Alzheimer’s Disease Neuroimaging Initiative, Kotschi M, Peter J, Egger K, Hausner L, et al. Separating Symptomatic Alzheimer’s Disease from Depression based on Structural MRI. Journal of Alzheimer's Disease. 2018;63(1):353-363. doi:10.3233/jad-170964
  46. A. H. Shoeb, "Application of machine learning to epileptic seizure onset detection and treatment," Ph.D. dissertation, Massachusetts Institute of Technology, 2009.
  47. D'Mello S, Picard RW, Graesser A. Toward an Affect-Sensitive AutoTutor. IEEE Intelligent Systems. 2007;22(4):53-61. doi:10.1109/mis.2007.79
  48. McStay A. Empathic media and advertising: Industry, policy, legal and citizen perspectives (the case for intimacy). Big Data & Society. 2016;3(2). doi:10.1177/2053951716666868
  49. McStay A. Emotional AI, soft biometrics and the surveillance of emotional life: An unusual consensus on privacy. Big Data & Society. 2020;7(1):205395172090438. doi:10.1177/2053951720904386
  50. Schulte‐Frankenfeld PM, Trautwein FM. App‐based mindfulness meditation reduces perceived stress and improves self‐regulation in working university students: A randomised controlled trial. Applied Psychology: Health and Well-Being. 2021;14(4):1151-1171. doi:10.1111/aphw.12328
  51. Hides L, Dingle G, Quinn C, Stoyanov SR, Zelenko O, Tjondronegoro D, et al. Efficacy and Outcomes of a Music-Based Emotion Regulation Mobile App in Distressed Young People: Randomized Controlled Trial. JMIR mHealth and uHealth. 2019;7(1):e11482. doi:10.2196/11482
  52. Youssef NA, Rich CL. Does Acute Treatment with Sedatives/Hypnotics for Anxiety in Depressed Patients Affect Suicide Risk? A Literature Review. Annals of Clinical Psychiatry. 2008;20(3):157-169. doi:10.1080/10401230802177698
  53. N. Cummins et al., "Artificial intelligence to aid the detection of mood disorders," in Artificial Intelligence in Precision Health, D. Barh, Ed., Cambridge, MA: Academic Press, 2020, pp. 231-255.
  54. Abdullah S, Matthews M, Frank E, Doherty G, Gay G, Choudhury T. Automatic detection of social rhythms in bipolar disorder. Journal of the American Medical Informatics Association. 2016;23(3):538-543. doi:10.1093/jamia/ocv200
  55. Anzulewicz A, Sobota K, Delafield-Butt JT. Toward the Autism Motor Signature: Gesture patterns during smart tablet gameplay identify children with autism. Scientific Reports. 2016;6(1). doi:10.1038/srep31107
  56. Taffoni et al., "Sensor-based technology in the study of motor skills in infants at risk for ASD," in 4th IEEE RAS & EMBS Int. Conf. Biomed. Robot. Biomechatronics (BioRob), 2012, pp. 1879-1883.
  57. Bedi G, Carrillo F, Cecchi GA, Slezak DF, Sigman M, Mota NB, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths. npj Schizophrenia. 2015;1(1). doi:10.1038/npjschz.2015.30
  58. Corcoran CM, Cecchi GA. Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2020;5(8):770-779. doi:10.1016/j.bpsc.2020.06.004
  59. McCradden M, Hui K, Buchman DZ. Evidence, ethics and the promise of artificial intelligence in psychiatry. Journal of Medical Ethics. 2022;49(8):573-579. doi:10.1136/jme-2022-108447
  60. Lovejoy CA. Technology and mental health: The role of artificial intelligence. European Psychiatry. 2019;55:1-3. doi:10.1016/j.eurpsy.2018.08.004
  61. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA Journal of Ethics. 2019;21(2):E121-124. doi:10.1001/amajethics.2019.121
  62. Walsh CG, Chaudhry B, Dua P, Goodman KW, Kaplan B, Kavuluru R, et al. Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence. JAMIA Open. 2020;3(1):9-15. doi:10.1093/jamiaopen/ooz054
  63. D. W. Joyce, A. Kormilitzin, K. A. Smith and A. Cipriani, "Explainable artificial intelligence for mental health through transparency and interpretability for understandability," NPJ Digit. Med., vol. 6, p. 6, 2023, doi:10.1038/s41746-023- 00751-9.
  64. Straw I, Callison-Burch C. Artificial Intelligence in mental health and the biases of language based models. PLOS ONE. 2020;15(12):e0240376. doi:10.1371/journal.pone.0240376
  65. Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, et al. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2021;6(9):856-864. doi:10.1016/j.bpsc.2021.02.001
  66. N. Koutsouleris, T. U. Hauser, V. Skvortsova and M. De Choudhury, "From promise to practice: Towards the realization of AI-informed mental health care," Lancet Digit. Health, vol. 4, pp. e829-e840, 2022, doi:10.1016/S2589- 7500(22)00153-4.
  67. Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, et al. The Clinician and Dataset Shift in Artificial Intelligence. New England Journal of Medicine. 2021;385(3):283-286. doi:10.1056/nejmc2104626
  68. Carr S. ‘AI gone mental’: engagement and ethics in data-driven technology for mental health. Journal of Mental Health. 2020;29(2):125-130. doi:10.1080/09638237.2020.1714011
  69. Balcombe L, De Leo D. Digital Mental Health Challenges and the Horizon Ahead for Solutions. JMIR Mental Health. 2021;8(3):e26811. doi:10.2196/26811
  70. Miner AS, Shah N, Bullock KD, Arnow BA, Bailenson J, Hancock J. Key Considerations for Incorporating Conversational AI in Psychotherapy. Frontiers in Psychiatry. 2019;10. doi:10.3389/fpsyt.2019.00746
  71. M. De Choudhury, S. Dutta and J. Ma, "Measuring the impact of anxiety on online social interactions," in Proc. Int. AAAI Conf. Web Soc. Media (ICWSM), 2018, pp. 1-9.
Support