Skip to main content


Log in

The Future of Digital Psychiatry

  • Psychiatry in the Digital Age (J Shore, Section Editor)
  • Published:
Current Psychiatry Reports Aims and scope Submit manuscript


Purpose of Review

Treatments in psychiatry have been rapidly changing over the last century, following the development of psychopharmacology and new research achievements. However, with advances in technology, the practice of psychiatry in the future will likely be influenced by new trends based on computerized approaches and digital communication. We examined four major areas that will probably impact on the clinical practice in the next few years: telepsychiatry; social media; mobile applications and internet of things; artificial intelligence; and machine learning.

Recent Findings

Developments in these four areas will benefit patients throughout the journey of the illness, encompassing early diagnosis, even before the patients present to a clinician; personalized treatment on demand at anytime and anywhere; better prediction on patient outcomes; and even how mental illnesses are diagnosed in the future.


Though the evidence for many technology-based interventions or mobile applications is still insufficient, it is likely that such advances in technology will play a larger role in the way that patient receives mental health interventions in the future, leading to easier access to them and improved outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. • Drago A, Winding TN, Antypa N. Videoconferencing in psychiatry, a meta-analysis of assessment and treatment. Eur Psychiatry. 2016;36:29–37. This article reviews the use of videoconferencing in psychiatry.

    Article  CAS  PubMed  Google Scholar 

  2. Ventriglio A, Torales J, Castaldelli-Maia J. Telepsychiatry and social psychiatry. Int J Soc Psychiatry [Internet]. SAGE PublicationsSage UK: London, England; 2017 [cited 2019 May 29];63:387–8. Available from:

  3. Hubley S, Lynch SB, Schneck C, Thomas M, Shore J. Review of key telepsychiatry outcomes. World J Psychiatry. 2016;6:269–82 Available from:

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mucic D. Transcultural telepsychiatry and its impact on patient satisfaction. J Telemed Telecare. 2010;16:237–42 Available from:

    Article  PubMed  Google Scholar 

  5. Cheng KM, Siu BWM, Yeung CCYA, Chiang TP, So MH, Yeung MCW. Telepsychiatry for stable Chinese psychiatric out-patients in custody in Hong Kong: A case-control pilot study. Hong Kong Med J. 2018;24:378–83.

  6. Butler TN, Yellowlees P. Cost analysis of store-and-forward telepsychiatry as a consultation model for primary care. Telemed e-Health [Internet]. 2012 [cited 2019 Feb 11];18:74–7. Available from:

  7. Malhotra S, Chakrabarti S, Shah R, Gupta A, Mehta A, Nithya B, et al. Development of a novel diagnostic system for a telepsychiatric application: a pilot validation study. BMC Res Notes. 2014;7:508–18.

  8. Yeung A, Martinson MA, Baer L, Chen J, Clain A, Williams A, et al. The effectiveness of telepsychiatry-based culturally sensitive collaborative treatment for depressed Chinese American immigrants: a randomized controlled trial. J Clin Psychiatry. 2016;77:e996–1002.

    Article  PubMed  Google Scholar 

  9. Yang Y. Ping An Good Doctor blazes trail in developing unstaffed, AI-assisted clinics in China. South China Morning Post [Internet]. 2018 Nov 19 [cited 2019 Apr 11]; Available from:

  10. Woods HC, Scott H. #Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J Adolesc [Internet]. Academic Press; 2016 [cited 2019 May 29];51:41–9. Available from:

  11. Kelly Y, Zilanawala A, Booker C, Sacker A. Social media use and adolescent mental health: findings from the UK Millennium Cohort Study. EClinicalMedicine. 2018;6:59–68. Elsevier Ltd; Available from.

    Article  PubMed  Google Scholar 

  12. Reece AG, Danforth CM. Instagram photos reveal predictive markers of depression. EPJ Data Sci. 2017;6:1–12. [Internet]. The Author(s). Available from.

    Article  Google Scholar 

  13. De Choudhury M, Gamon M, Counts S, Horvitz E. (2013, June) Predicting depression via social media. Proc Seventh Int AAAI Conf Weblogs Soc Media.

  14. •• Gomes de Andrade NN, Pawson D, Muriello D, Donahue L, Guadagno J. Ethics and Artificial intelligence: suicide prevention on Facebook. Philos Technol. 2018;31:669–84. Written by the people who actually work at Facebook to develop the suicide prediction algorithm, this article introduces the applications of machine learning and dedicates a large portion of the publication to talk about the relevant ethical issues.

  15. Statista. Number of social media users worldwide 2010-2021 [Internet]. 2019 [cited 2019 Apr 7]. Available from:

  16. Frankish K, Ryan C, Harris A. (2012). Psychiatry and online social media: potential, pitfalls and ethical guidelines for psychiatrists and trainees. Australasian Psychiatry, 20(3), 181–187.

    Article  PubMed  Google Scholar 

  17. Cox-George C. The changing face(book) of psychiatry: can we justify ‘following’ patients’ social media activity? BJPsych Bull. 2015;39:283–4.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Appelbaum PS, Kopelman A. Social media’s challenges for psychiatry. World Psychiatry. 2014;13:21–3.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Anthes E. Pocket Psychiatry. Nature. 2016;532:20–3 Available from:!/menu/main/topColumns/topLeftColumn/pdf/532020a.pdf.

    Article  CAS  PubMed  Google Scholar 

  20. About Us [Internet]. 2019 [cited 2019 Apr 4]. Available from:

  21. Gustafson DH, McTavish FM, Chih M-Y, Atwood AK, Johnson R A., Boyle MG, et al. A Smartphone Application to Support Recovery From Alcoholism. JAMA Psychiatry [Internet]. 2014;71:566. Available from:

    Article  PubMed  PubMed Central  Google Scholar 

  22. • Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World psychiatry [Internet]. World Psychiatric Association; 2017 [cited 2019 Apr 10];16:287–98. Available from: A meta-analysis on the efficacy of smartphone applications on treating depressive symptoms.

  23. • Firth J, Torous J, Nicholas J, Carney R, Rosenbaum S, Sarris J. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J Affect Disord. 2017;218:15–22. A meta-analysis on the efficacy of smartphone applications on treating anxiety symptoms.

    Article  PubMed  Google Scholar 

  24. •• Chandrashekar P. Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. mHealth [Internet]. AME Publications; 2018 [cited 2019 Apr 10];4:1–4. Available from: A short review looking at the efficacy of various mobile phone applications in improving the depressive and anxiety symptoms.

  25. L Lui JH, Marcus DK, Barry CT, Lui C. Evidence-based apps? A review of mental health mobile applications in a psychotherapy context. Prof Psychol Res Pract. 2017;48:199–210.

    Article  Google Scholar 

  26. Place S. Honoring our Veterans: Leveraging Mobile Technology for Real Time Support [Internet]. Cogito. 2017 [cited 2019 Apr 4]. Available from:

  27. Otsuka America Pharmceutical I. The ABILIFY MYCITE® System [Internet]. 2019 [cited 2019 Apr 10]. Available from:

  28. Miotto R, Danieletto M, Scelza JR, Kidd BA, Dudley JT. Reflecting health: smart mirrors for personalized medicine. NPJ Digit Med. 2018;1:62.

    Article  PubMed  PubMed Central  Google Scholar 

  29. de la Torre Díez I, Alonso SG, Hamrioui S, Cruz EM, Nozaleda LM, Franco MA. IoT-based services and applications for mental health in the literature. J Med Syst. 2019;43:4–9.

    Article  Google Scholar 

  30. Zarley BD. Meet the scientists who are training AI to diagnose mental illness [Internet]. The Verge. 2019 [cited 2019 Apr 4]. Available from:

  31. Kalmady SV, Greiner R, Agrawal R, Shivakumar V, Narayanaswamy JC, Brown MRG, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ Schizophr. 2019;5:1–11.

    Article  Google Scholar 

  32. 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 Schizophr; 2015;1. doi:

  33. Marmar CR, Brown AD, Qian M, Laska E, Siegel C, Li M, et al. Speech-based markers for posttraumatic stress disorder in US veterans. Depress Anxiety. 2019;1–10:

  34. Wall DP, Kosmicki J, Deluca TF, Harstad E, Fusaro VA. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatry. 2012;2:e100–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lucas GM, Gratch J, King A, Morency LP. It’s only a computer: virtual humans increase willingness to disclose. Comput Hum Behav. 2014;37:94–100.

    Article  Google Scholar 

  36. Brown E. Computerized “Ellie” has just enough humanity to aid in therapy worktle. Los Angeles Times [Internet]. Los Angeles; 2015 Apr 3; Available from:

  37. Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA. 2016;315:551–2.

    Article  CAS  PubMed  Google Scholar 

  38. Koutsouleris N, Kambeitz-Ilankovic L, Ruhrmann S, Rosen M, Ruef A, Dwyer DB, et al. Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiatry. 2018;75:1156–72.

    Article  PubMed  PubMed Central  Google Scholar 

  39. • Bhugra D, Tasman A, Pathare S, Priebe S, Smith S, Torous J, et al. The WPA- Lancet Psychiatry Commission on the future of psychiatry. Lancet Psychiatry. 2017;4:775–818. Available from: A brief overview on the future of digital psychiatry from another perspective.

  40. 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 Heal [Internet]. 2017 [cited 2019 Feb 11];4:e19. Available from:

    Article  PubMed  PubMed Central  Google Scholar 

  41. Grisanzio KA, Goldstein-Piekarski AN, Wang MY, Ahmed APR, Samara Z, Williams LM. Transdiagnostic symptom clusters and associations with brain, behavior, and daily function in mood, anxiety, and trauma disorders. JAMA Psychiatry. 2018;75:201–9.

    Article  PubMed  Google Scholar 

  42. Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam L, Kohane IS. Emerging vulnerabilities demand new conversations. Science. 2019;363:1287–90.

    Article  CAS  PubMed  Google Scholar 

  43. •• Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M.,& Sundberg, P. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 18. Available from:

  44. Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci. 2017;5:457–69.

    Article  Google Scholar 

  45. Garg P, Glick S. AI’s potential to diagnose and treat mental illness [Internet]. Harv. Bus. Rev. 2018 [cited 2019 Apr 4]. Available from:

  46. • Maddox TM, Rumsfeld JS, Payne PRO. Questions for artificial intelligence in health care. JAMA. 2019;321:31–2. A critical review on how artificial intelligence should be applied into healthcare.

    Article  PubMed  Google Scholar 

  47. Colleges A of MR. Artificial intelligence in healthcare management [Internet]. London; 2019. doi:

    Article  PubMed  Google Scholar 

  48. Wartman SA, Combs CD. Reimagining medical education in the age of AI. AMA J Ethics. 2019;21:E146–52.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Keith Hariman.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Psychiatry in the Digital Age

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hariman, K., Ventriglio, A. & Bhugra, D. The Future of Digital Psychiatry. Curr Psychiatry Rep 21, 88 (2019).

Download citation

  • Published:

  • DOI: