The Role of Artificial Intelligence in Mental Health: A Review
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Published
Nov 29, 2023
Abstract
The domains of mental health and artificial intelligence (AI) are undergoing rapid advancements, exhibiting the capacity to mutually influence one another in significant ways. The increasing prevalence of mental health illnesses has prompted the exploration of potential remedies in the field of AI, which show promise in the areas of early detection, prevention, and therapy. Sophisticated machine learning algorithms possess the capability to evaluate extensive volumes of data, including social media posts and voice patterns, with the objective of detecting patterns and symptoms associated with mental illness. This facilitates the implementation of more focused interventions and individualized treatment strategies. Furthermore, chatbots utilizing AI have the capability to deliver round-the-clock assistance to those undergoing acute distress or grant them access to therapy in cases where waiting lists are extensive. Nevertheless, it is of utmost importance to guarantee the incorporation of ethical issues throughout the use of AI in the field of mental healthcare. In order to achieve successful integration, it is imperative to address many concerns, including but not limited to privacy, bias, and accurate diagnosis. However, the convergence of mental health and AI offers a distinct prospect to transform our approach to mental disease and improve the availability of care for countless individuals globally.
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Keywords
Artificial Intelligence, Mental Health, Assessment, Accessibility, Outcomes
References
1. Torous J, Bucci S, Bell IH, Kessing LV, Faurholt-Jepsen M, Whelan P, Carvalho AF, Keshavan M, Linardon J, Firth J. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021; 20(3):318-335. DOI: https://doi.org/10.1002/wps.20883
2. Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and arti-ficial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging 2021; 6(9):856-864. DOI: https://doi.org/10.1016/j.bpsc.2021.02.001
3. Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine learning and natural language processing in mental health: Systematic review. J Med Internet Res 2021; 23(5):e15708. DOI: https://doi.org/10.2196/15708
4. Maples-Keller JL, Bunnell BE, Kim SJ, Rothbaum BO. The use of virtual reality technology in the treatment of anxiety and other psychiatric disorders. Harv Rev Psychiatry 2017; 25(3):103-113. DOI: https://doi.org/10.1097/HRP.0000000000000138
5. Parsapoor Mah Parsa M, Koudys JW, Ruocco AC. Suicide risk detection using artificial intelligence: The promise of creating a benchmark dataset for research on the detection of suicide risk. Front Psychiatry 2023; 14:1186569. DOI: https://doi.org/10.3389/fpsyt.2023.1186569
6. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif Intell Healthc 2020:295-336. DOI: https://doi.org/10.1016/B978-0-12-818438-7.00012-5
7. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci 2021; 14(1):86-93. DOI: https://doi.org/10.1111/cts.12884
8. Gauthier A, Rizvi S, Cukurova M, Mavrikis M. Is it time we get real? A systematic review of the po-tential of data-driven technologies to address teachers’ implicit biases. Front Artif Intell 2022; 5:994967. DOI: https://doi.org/10.3389/frai.2022.994967
9. Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 2019; 7:e7702. DOI: https://doi.org/10.7717/peerj.7702
10. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018; 178(11):1544-1547. DOI: https://doi.org/10.1001/jamainternmed.2018.3763
11. Petersson L, Vincent K, Svedberg P, Nygren JM, Larsson I. Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice. Digit Health 2023; 9:20552076231206588. DOI: https://doi.org/10.1177/20552076231206588
12. Haque MDR, Rubya S. An overview of chat-bot-based mobile mental health apps: Insights from app description and user reviews. JMIR Mhealth Uhealth 2023; 11:e44838. DOI: https://doi.org/10.2196/44838
13. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc J 2021; 8(2):e188-e194. DOI: https://doi.org/10.7861/fhj.2021-0095
14. Batko K, Ślęzak A. The use of big data analytics in healthcare. J Big Data 2022; 9(1):3. DOI: https://doi.org/10.1186/s40537-021-00553-4
15. Pham KT, Nabizadeh A, Selek S. Artificial intelli-gence and chatbots in psychiatry. Psychiatr Q. 2022 Mar;93(1):249-253. DOI: https://doi.org/10.1007/s11126-022-09973-8
16. Reed JE, Card AJ. The problem with Plan-Do-Study-Act cycles. BMJ Qual Saf 2016; 25(3):147-152. DOI: https://doi.org/10.1136/bmjqs-2015-005076
17. Yazdavar AH, Mahdavinejad MS, Bajaj G, Romine W, Sheth A, Monadjemi AH, Thirunarayan K, Meddar JM, Myers A, Pathak J, Hitzler P. Multi-modal mental health analysis in social media. PLoS One 2020; 15(4):e0226248. DOI: https://doi.org/10.1371/journal.pone.0226248
18. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digit Med 2020; 3:17. DOI: https://doi.org/10.1038/s41746-020-0221-y
19. Varkey B. Principles of clinical ethics and their application to practice. Med Princ Pract 2021; 30(1):17-28. DOI: https://doi.org/10.1159/000509119
20. Rubeis G. iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interv 2022; 28:100518. DOI: https://doi.org/10.1016/j.invent.2022.100518
21. King CA, Horwitz A, Czyz E, Lindsay R. Suicide risk screening in healthcare settings: Identifying males and females at risk. J Clin Psychol Med Settings 2017; 24(1):8-20. DOI: https://doi.org/10.1007/s10880-017-9486-y
22. Coulter A, Oldham J. Person-centred care: What is it and how do we get there? Future Hosp J 2016; 3(2):114-116. DOI: https://doi.org/10.7861/futurehosp.3-2-114
23. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022; 39(8):120. DOI: https://doi.org/10.1007/s12032-022-01711-1
24. Chen J, Mullins CD, Novak P, Thomas SB. Per-sonalized strategies to activate and empower pa-tients in health care and reduce health disparities. Health Educ Behav 2016; 43(1):25-34. DOI: https://doi.org/10.1177/1090198115579415
25. Baker R, Camosso-Stefinovic J, Gillies C, Shaw EJ, Cheater F, Flottorp S, Robertson N, Wensing M, Fiander M, Eccles MP, Godycki-Cwirko M, van Lieshout J, Jäger C. Tailored interventions to ad-dress determinants of practice. Cochrane Database Syst Rev 2015; 2015(4):CD005470. DOI: https://doi.org/10.1002/14651858.CD005470.pub3
26. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J 2019; 6(2):94-98. DOI: https://doi.org/10.7861/futurehosp.6-2-94
27. Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, Sharp RR. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med 2021; 4(1):140. DOI: https://doi.org/10.1038/s41746-021-00509-1
28. Hall WJ, Chapman MV, Lee KM, Merino YM, Thomas TW, Payne BK, Eng E, Day SH, Coyne-Beasley T. Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: A systematic review. Am J Public Health 2015; 105(12):e60-e76. DOI: https://doi.org/10.2105/AJPH.2015.302903
29. Bohr A, Memarzadeh K. The rise of artificial intel-ligence in healthcare applications. Artif Intell Healthc 2020:25-60. DOI: https://doi.org/10.1016/B978-0-12-818438-7.00002-2
30. Sabry F, Eltaras T, Labda W, Alzoubi K, Malluhi Q. Machine learning for healthcare wearable devices: The big picture. J Healthc Eng 2022; 2022:4653923. DOI: https://doi.org/10.1155/2022/4653923
31. Jadczyk T, Wojakowski W, Tendera M, Henry TD, Egnaczyk G, Shreenivas S. Artificial intelligence can improve patient management at the time of a pandemic: The role of voice technology. J Med Internet Res 2021; 23(5):e22959. DOI: https://doi.org/10.2196/22959
32. Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the in-tersection of artificial intelligence and clinical healthcare: A multidisciplinary review. Diagnostics (Basel) 2023; 13(12):1995. DOI: https://doi.org/10.3390/diagnostics13121995
33. Institute of Medicine (US) Committee on Regional Health Data Networks; Donaldson MS, Lohr KN, editors. Health Data in the Information Age: Use, Disclosure, and Privacy. Washington (DC): National Academies Press (US); 1994. 4, Confidentiality and Privacy of Personal Data. Available at: https://www.ncbi.nlm.nih.gov/books/NBK236546/
34. Nazer LH, Zatarah R, Waldrip S, Ke JXC, Mou-kheiber M, Khanna AK, Hicklen RS, Moukheiber L, Moukheiber D, Ma H, Mathur P. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digit Health 2023; 2(6):e0000278. DOI: https://doi.org/10.1371/journal.pdig.0000278
35. Gurevich E, El Hassan B, El Morr C. Equity within AI systems: What can health leaders expect? Healthc Manage Forum 2023; 36(2):119-124. DOI: https://doi.org/10.1177/08404704221125368
36. Zhang T, Schoene AM, Ji S, Ananiadou S. Natural language processing applied to mental illness de-tection: A narrative review. NPJ Digit Med 2022; 5(1):46. DOI: https://doi.org/10.1038/s41746-022-00589-7
37. Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural language processing of clinical notes on chronic diseases: Systematic re-view. JMIR Med Inform 2019; 7(2):e12239. DOI: https://doi.org/10.2196/12239
38. Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous JB. Chatbots and conver-sational agents in mental health: A review of the psychiatric landscape. Can J Psychiatry 2019; 64(7):456-464. DOI: https://doi.org/10.1177/0706743719828977
39. Rabbani N, Bedgood M, Brown C, Steinberg E, Goldstein RL, Carlson JL, Pageler N, Morse KE. A natural language processing model to identify con-fidential content in adolescent clinical notes. Appl Clin Inform 2023; 14(3):400-407. DOI: https://doi.org/10.1055/a-2051-9764
40. Straw I, Callison-Burch C. Artificial Intelligence in mental health and the biases of language based models. PLoS One 2020; 15(12):e0240376. DOI: https://doi.org/10.1371/journal.pone.0240376
41. Coghlan S, Leins K, Sheldrick S, Cheong M, Gooding P, D'Alfonso S. To chat or bot to chat: Ethical issues with using chatbots in mental health. Digit Health 2023; 9:20552076231183542. DOI: https://doi.org/10.1177/20552076231183542
42. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, Jeste DV. Artificial intelligence for mental health and mental illnesses: An overview. Curr Psychiatry Rep 2019; 21(11):116. DOI: https://doi.org/10.1007/s11920-019-1094-0
43. Ashrafian H. Can artificial intelligences suffer from mental illness? A philosophical matter to consider. Sci Eng Ethics 2017; 23(2):403-412. DOI: https://doi.org/10.1007/s11948-016-9783-0
44. Yan WJ, Ruan QN, Jiang K. Challenges for artificial intelligence in recognizing mental disorders. Diag-nostics (Basel) 2022; 13(1):2. DOI: https://doi.org/10.3390/diagnostics13010002
45. Robinson OJ, Vytal K, Cornwell BR, Grillon C. The impact of anxiety upon cognition: perspectives from human threat of shock studies. Front Hum Neurosci 2013; 7:203. DOI: https://doi.org/10.3389/fnhum.2013.00203
46. Ijadi-Maghsoodi R, Marlotte L, Garcia E, Aralis H, Lester P, Escudero P, Kataoka S. Adapting and implementing a school-based resilience-building curriculum among low-income racial and ethnic minority students. Contemp Sch Psychol 2017; 21(3):223-239. DOI: https://doi.org/10.1007/s40688-017-0134-1
47. Morrow E, Zidaru T, Ross F, Mason C, Patel KD, Ream M, Stockley R. Artificial intelligence tech-nologies and compassion in healthcare: A sys-tematic scoping review. Front Psychol 2023; 13:971044. DOI: https://doi.org/10.3389/fpsyg.2022.971044
48. Esmaeilzadeh P, Mirzaei T, Dharanikota S. Patients’ perceptions toward human-artificial intelligence in-teraction in health care: Experimental study. J Med Internet Res 2021; 23(11):e25856. DOI: https://doi.org/10.2196/25856
2. Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and arti-ficial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging 2021; 6(9):856-864. DOI: https://doi.org/10.1016/j.bpsc.2021.02.001
3. Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine learning and natural language processing in mental health: Systematic review. J Med Internet Res 2021; 23(5):e15708. DOI: https://doi.org/10.2196/15708
4. Maples-Keller JL, Bunnell BE, Kim SJ, Rothbaum BO. The use of virtual reality technology in the treatment of anxiety and other psychiatric disorders. Harv Rev Psychiatry 2017; 25(3):103-113. DOI: https://doi.org/10.1097/HRP.0000000000000138
5. Parsapoor Mah Parsa M, Koudys JW, Ruocco AC. Suicide risk detection using artificial intelligence: The promise of creating a benchmark dataset for research on the detection of suicide risk. Front Psychiatry 2023; 14:1186569. DOI: https://doi.org/10.3389/fpsyt.2023.1186569
6. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif Intell Healthc 2020:295-336. DOI: https://doi.org/10.1016/B978-0-12-818438-7.00012-5
7. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci 2021; 14(1):86-93. DOI: https://doi.org/10.1111/cts.12884
8. Gauthier A, Rizvi S, Cukurova M, Mavrikis M. Is it time we get real? A systematic review of the po-tential of data-driven technologies to address teachers’ implicit biases. Front Artif Intell 2022; 5:994967. DOI: https://doi.org/10.3389/frai.2022.994967
9. Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 2019; 7:e7702. DOI: https://doi.org/10.7717/peerj.7702
10. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018; 178(11):1544-1547. DOI: https://doi.org/10.1001/jamainternmed.2018.3763
11. Petersson L, Vincent K, Svedberg P, Nygren JM, Larsson I. Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice. Digit Health 2023; 9:20552076231206588. DOI: https://doi.org/10.1177/20552076231206588
12. Haque MDR, Rubya S. An overview of chat-bot-based mobile mental health apps: Insights from app description and user reviews. JMIR Mhealth Uhealth 2023; 11:e44838. DOI: https://doi.org/10.2196/44838
13. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc J 2021; 8(2):e188-e194. DOI: https://doi.org/10.7861/fhj.2021-0095
14. Batko K, Ślęzak A. The use of big data analytics in healthcare. J Big Data 2022; 9(1):3. DOI: https://doi.org/10.1186/s40537-021-00553-4
15. Pham KT, Nabizadeh A, Selek S. Artificial intelli-gence and chatbots in psychiatry. Psychiatr Q. 2022 Mar;93(1):249-253. DOI: https://doi.org/10.1007/s11126-022-09973-8
16. Reed JE, Card AJ. The problem with Plan-Do-Study-Act cycles. BMJ Qual Saf 2016; 25(3):147-152. DOI: https://doi.org/10.1136/bmjqs-2015-005076
17. Yazdavar AH, Mahdavinejad MS, Bajaj G, Romine W, Sheth A, Monadjemi AH, Thirunarayan K, Meddar JM, Myers A, Pathak J, Hitzler P. Multi-modal mental health analysis in social media. PLoS One 2020; 15(4):e0226248. DOI: https://doi.org/10.1371/journal.pone.0226248
18. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digit Med 2020; 3:17. DOI: https://doi.org/10.1038/s41746-020-0221-y
19. Varkey B. Principles of clinical ethics and their application to practice. Med Princ Pract 2021; 30(1):17-28. DOI: https://doi.org/10.1159/000509119
20. Rubeis G. iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interv 2022; 28:100518. DOI: https://doi.org/10.1016/j.invent.2022.100518
21. King CA, Horwitz A, Czyz E, Lindsay R. Suicide risk screening in healthcare settings: Identifying males and females at risk. J Clin Psychol Med Settings 2017; 24(1):8-20. DOI: https://doi.org/10.1007/s10880-017-9486-y
22. Coulter A, Oldham J. Person-centred care: What is it and how do we get there? Future Hosp J 2016; 3(2):114-116. DOI: https://doi.org/10.7861/futurehosp.3-2-114
23. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022; 39(8):120. DOI: https://doi.org/10.1007/s12032-022-01711-1
24. Chen J, Mullins CD, Novak P, Thomas SB. Per-sonalized strategies to activate and empower pa-tients in health care and reduce health disparities. Health Educ Behav 2016; 43(1):25-34. DOI: https://doi.org/10.1177/1090198115579415
25. Baker R, Camosso-Stefinovic J, Gillies C, Shaw EJ, Cheater F, Flottorp S, Robertson N, Wensing M, Fiander M, Eccles MP, Godycki-Cwirko M, van Lieshout J, Jäger C. Tailored interventions to ad-dress determinants of practice. Cochrane Database Syst Rev 2015; 2015(4):CD005470. DOI: https://doi.org/10.1002/14651858.CD005470.pub3
26. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J 2019; 6(2):94-98. DOI: https://doi.org/10.7861/futurehosp.6-2-94
27. Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, Sharp RR. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med 2021; 4(1):140. DOI: https://doi.org/10.1038/s41746-021-00509-1
28. Hall WJ, Chapman MV, Lee KM, Merino YM, Thomas TW, Payne BK, Eng E, Day SH, Coyne-Beasley T. Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: A systematic review. Am J Public Health 2015; 105(12):e60-e76. DOI: https://doi.org/10.2105/AJPH.2015.302903
29. Bohr A, Memarzadeh K. The rise of artificial intel-ligence in healthcare applications. Artif Intell Healthc 2020:25-60. DOI: https://doi.org/10.1016/B978-0-12-818438-7.00002-2
30. Sabry F, Eltaras T, Labda W, Alzoubi K, Malluhi Q. Machine learning for healthcare wearable devices: The big picture. J Healthc Eng 2022; 2022:4653923. DOI: https://doi.org/10.1155/2022/4653923
31. Jadczyk T, Wojakowski W, Tendera M, Henry TD, Egnaczyk G, Shreenivas S. Artificial intelligence can improve patient management at the time of a pandemic: The role of voice technology. J Med Internet Res 2021; 23(5):e22959. DOI: https://doi.org/10.2196/22959
32. Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the in-tersection of artificial intelligence and clinical healthcare: A multidisciplinary review. Diagnostics (Basel) 2023; 13(12):1995. DOI: https://doi.org/10.3390/diagnostics13121995
33. Institute of Medicine (US) Committee on Regional Health Data Networks; Donaldson MS, Lohr KN, editors. Health Data in the Information Age: Use, Disclosure, and Privacy. Washington (DC): National Academies Press (US); 1994. 4, Confidentiality and Privacy of Personal Data. Available at: https://www.ncbi.nlm.nih.gov/books/NBK236546/
34. Nazer LH, Zatarah R, Waldrip S, Ke JXC, Mou-kheiber M, Khanna AK, Hicklen RS, Moukheiber L, Moukheiber D, Ma H, Mathur P. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digit Health 2023; 2(6):e0000278. DOI: https://doi.org/10.1371/journal.pdig.0000278
35. Gurevich E, El Hassan B, El Morr C. Equity within AI systems: What can health leaders expect? Healthc Manage Forum 2023; 36(2):119-124. DOI: https://doi.org/10.1177/08404704221125368
36. Zhang T, Schoene AM, Ji S, Ananiadou S. Natural language processing applied to mental illness de-tection: A narrative review. NPJ Digit Med 2022; 5(1):46. DOI: https://doi.org/10.1038/s41746-022-00589-7
37. Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural language processing of clinical notes on chronic diseases: Systematic re-view. JMIR Med Inform 2019; 7(2):e12239. DOI: https://doi.org/10.2196/12239
38. Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous JB. Chatbots and conver-sational agents in mental health: A review of the psychiatric landscape. Can J Psychiatry 2019; 64(7):456-464. DOI: https://doi.org/10.1177/0706743719828977
39. Rabbani N, Bedgood M, Brown C, Steinberg E, Goldstein RL, Carlson JL, Pageler N, Morse KE. A natural language processing model to identify con-fidential content in adolescent clinical notes. Appl Clin Inform 2023; 14(3):400-407. DOI: https://doi.org/10.1055/a-2051-9764
40. Straw I, Callison-Burch C. Artificial Intelligence in mental health and the biases of language based models. PLoS One 2020; 15(12):e0240376. DOI: https://doi.org/10.1371/journal.pone.0240376
41. Coghlan S, Leins K, Sheldrick S, Cheong M, Gooding P, D'Alfonso S. To chat or bot to chat: Ethical issues with using chatbots in mental health. Digit Health 2023; 9:20552076231183542. DOI: https://doi.org/10.1177/20552076231183542
42. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, Jeste DV. Artificial intelligence for mental health and mental illnesses: An overview. Curr Psychiatry Rep 2019; 21(11):116. DOI: https://doi.org/10.1007/s11920-019-1094-0
43. Ashrafian H. Can artificial intelligences suffer from mental illness? A philosophical matter to consider. Sci Eng Ethics 2017; 23(2):403-412. DOI: https://doi.org/10.1007/s11948-016-9783-0
44. Yan WJ, Ruan QN, Jiang K. Challenges for artificial intelligence in recognizing mental disorders. Diag-nostics (Basel) 2022; 13(1):2. DOI: https://doi.org/10.3390/diagnostics13010002
45. Robinson OJ, Vytal K, Cornwell BR, Grillon C. The impact of anxiety upon cognition: perspectives from human threat of shock studies. Front Hum Neurosci 2013; 7:203. DOI: https://doi.org/10.3389/fnhum.2013.00203
46. Ijadi-Maghsoodi R, Marlotte L, Garcia E, Aralis H, Lester P, Escudero P, Kataoka S. Adapting and implementing a school-based resilience-building curriculum among low-income racial and ethnic minority students. Contemp Sch Psychol 2017; 21(3):223-239. DOI: https://doi.org/10.1007/s40688-017-0134-1
47. Morrow E, Zidaru T, Ross F, Mason C, Patel KD, Ream M, Stockley R. Artificial intelligence tech-nologies and compassion in healthcare: A sys-tematic scoping review. Front Psychol 2023; 13:971044. DOI: https://doi.org/10.3389/fpsyg.2022.971044
48. Esmaeilzadeh P, Mirzaei T, Dharanikota S. Patients’ perceptions toward human-artificial intelligence in-teraction in health care: Experimental study. J Med Internet Res 2021; 23(11):e25856. DOI: https://doi.org/10.2196/25856
How to Cite
Shimada, K. (2023). The Role of Artificial Intelligence in Mental Health: A Review. Science Insights, 43(5), 1119–1127. https://doi.org/10.15354/si.23.re820
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