Essential Regulatory Update for Implementation of Clinical Decision Support Software in the Psychiatric Field
Main Article Content
Abstract
Decision-making in the field of psychiatry, including diagnosis, is highly complex and is hindered by use of a dichotomous categorical classification system that is ill-suited to mental disorders which are multi-factorial behavioral conditions. In addition, traditional use of results from randomized controlled trials to guide medical device regulation in the field of psychiatry is also problematic, as marked heterogeneity exists within psychiatric patient groups, which precludes results from these trials being projected onto the general patient population. In the past 20 years, clinical decision support software (CDSS) has been found to improve decision-making abilities, but the traditional regulatory approach based on the categorical classification system and randomized controlled trials does not allow the necessary flexibility for CDSS-based decision-making in psychiatry. In this article, we will use Major Depressive Disorder as an example and will discuss regulatory considerations for CDSS, including artificial intelligence, in psychiatry. We will also provide an adjusted life-cycle framework for CDSS in psychiatry, given that the particular complexity of psychiatric disorders demands new and innovative decision support tools. We suggest that any new software would need to perform at least as well as the standard-of-care, which in psychiatry is an unfortunate trial-and-error process. This would be demonstrated during the pre-market validation stage using clinical data from back-end testing of the CDSS. We propose that pre-market evidence of CDSS efficacy should be based on parameters that are used to measure the software success rate, with evidence of safety including demonstration of the low risk of CDSS due to human involvement in the decision-making process. In the post-market stage, CDSS would be used by doctors to generate real-world data that would allow ongoing evaluation and improvement of the algorithms. Furthermore, CDSS would collect data beyond the initial intended-use patient population, allowing the CDSS to learn about related indications. These data would inform the pre-market phase, during which the CDSS could be updated with an expanded patient population. We anticipate that such changes would support effective use of CDSS in psychiatry and improved patient care, which is particularly important given the trial-and-process that comprises the current standard-of-care in the field.
Article Details
How to Cite
TALIAZ, Dekel et al.
Essential Regulatory Update for Implementation of Clinical Decision Support Software in the Psychiatric Field.
Medical Research Archives, [S.l.], v. 11, n. 11, oct. 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4658>. Date accessed: 22 dec. 2024.
doi: https://doi.org/10.18103/mra.v11i10.4658.
Section
Editorial
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Singh A, Mehta JC, Anand D, Nath P, Pandey B, Khamparia A. An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k-means clustering and improved ensemble learning. Expert Syst. 2021;38(1):e12526. doi:10.1111/exsy.12526
2. Newson JJ, Hunter D, Thiagarajan TC. The heterogeneity of mental health assessment. Front Psychiatry. 2020;11:76. doi:10.3389/fpsyt.2020.00076
3. Olbert CM, Gala GJ, Tupler LA. Quantifying heterogeneity attributable to polythetic diagnostic criteria: theoretical framework and empirical application. J Abnorm Psychol. 2014;123(2):452. doi:10.1037/a0036068
4. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Pub; 2013:ISBN 978-0-89042-557-2.
5. Perugi G, Barbuti M. There are no patients without comorbidity. Eur Neuropsychopharmacol J Eur Coll Neuropsychopharmacol. 2021;50:104-106. doi:10.1016/j.euroneuro.2021.05.002
6. Loftus J, Scott J, Vorspan F, et al. Psychiatric comorbidities in bipolar disorders: an examination of the prevalence and chronology of onset according to sex and bipolar subtype. J Affect Disord. 2020;267:258-263. doi:10.1016/j.jad.2020.02.035
7. McIntyre RS, Berk M, Brietzke E, et al. Bipolar disorders. The Lancet. 2020;396(10265):1841-1856. doi:10.1016/S0140-6736(20)31544-0
8. Perugi G, Pallucchini A, Rizzato S, Pinzone V, De Rossi P. Current and emerging pharmacotherapy for the treatment of adult attention deficit hyperactivity disorder (ADHD). Expert Opin Pharmacother. 2019;20(12):1457-1470. doi:10.1080/14656566.2019.1618270
9. Butlen-Ducuing F, Haas M, Pani L, van Zwieten-Boot B, Broich K. DSM-5 and clinical trials in psychiatry: challenges to come? Nat Rev Drug Discov. 2012;11(8):583-584. doi:10.1038/nrd3811
10. Widiger TA. Categorical versus dimensional classification: Implications from and for research. J Personal Disord. 1992;6(4):287. doi:10.1521/pedi.1992.6.4.287
11. Guze SB. Nature of psychiatric illness: Why psychiatry is a branch of medicine. Compr Psychiatry. 1978;19(4):295-307. doi:10.1016/0010-440x(78)90012-3
12. Widiger TA, Trull TJ. Plate tectonics in the classification of personality disorder: shifting to a dimensional model. Am Psychol. 2007;62(2):71. doi:10.1037/0003-066X.62.2.71
13. Livesley WJ, Jackson DN, Schroeder ML. Factorial structure of traits delineating personality disorders in clinical and general population samples. J Abnorm Psychol. 1992;101(3):432. doi:10.1037//0021-843x.101.3.432
14. Goldberg D. A dimensional model for common mental disorders. Br J Psychiatry. 1996;168(S30):44-49.
15. Taliaz D, Souery D. A new characterization of mental health disorders using digital behavioral data: evidence from Major Depressive Disorder. J Clin Med. 2021;10(14):3109. doi:10.3390/jcm10143109
16. Jones C, Thornton J, Wyatt JC. Enhancing trust in clinical decision support systems: a framework for developers. BMJ Health Care Inform. 2021;28(1). doi:10.1136/bmjhci-2020-100247
17. Harris MG, Kazdin AE, Chiu WT, et al. Findings from world mental health surveys of the perceived helpfulness of treatment for patients with Major Depressive Disorder. JAMA Psychiatry. 2020;77(8):830-841. doi:10.1001/jamapsychiatry.2020.1107
18. van Westrhenen R, Ingelman-Sundberg M. Editorial: from trial and error to individualised pharmacogenomics-based pharmacotherapy in psychiatry. Front Pharmacol. 2021;12:725565. doi:10.3389/fphar.2021.725565
19. Suzuki K, Chen Y. Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Vol 140. Springer; 2018.
20. Shen J, Zhang CJ, Jiang B, et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Inform. 2019;7(3):e10010. doi:10.2196/10010
21. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056
22. Han SS, Park GH, Lim W, et al. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PloS One. 2018;13(1):e0191493. doi:10.1371/journal.pone.0191493
23. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. doi:10.1136/svn-2017-000101
24. Szolovits P. Artificial intelligence and medicine. In: Artificial Intelligence in Medicine. Routledge; 2019:1-19.
25. D’alfonso S, Santesteban-Echarri O, Rice S, et al. Artificial intelligence-assisted online social therapy for youth mental health. Front Psychol. 2017;8:796. doi:10.3389/fpsyg.2017.00796
26. Bedi G, Carrillo F, Cecchi GA, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophr. 2015;1(1):1-7. doi:10.1038/npjschz.2015.30
27. Poulin C, Shiner B, Thompson P, et al. Predicting the risk of suicide by analyzing the text of clinical notes. PloS One. 2014;9(1):e85733. doi:10.1371/journal.pone.0085733
28. Taliaz D, Spinrad A, Barzilay R, et al. Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry. 2021;11(1):1-9. doi:10.1038/s41398-021-01488-3
29. Fakhoury M. Artificial intelligence in psychiatry. Adv Exp Med Biol. 2019;1192:119-125. doi:10.1007/978-981-32-9721-0_6
30. Center for Devices and Radiological Health. General/Specific Intended Use - Guidance for Industry. U.S. Food and Drug Administration. Published March 18, 2020. Accessed October 23, 2022. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/generalspecific-intended-use-guidance-industry
31. The European Parliament and the Council of the European Union. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on Medical Devices, Amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and Repealing Council Directives 90/385/EEC and 93/42/EEC.; 2017. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0745
32. Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617-627. doi:10.1001/archpsyc.62.6.617
33. Walsh BT, Seidman SN, Sysko R, Gould M. Placebo response in studies of major depression: variable, substantial, and growing. JAMA. 2002;287(14):1840-1847. doi:10.1001/jama.287.14.1840
34. Fava M, Evins AE, Dorer DJ, Schoenfeld DA. The problem of the placebo response in clinical trials for psychiatric disorders: culprits, possible remedies, and a novel study design approach. Psychother Psychosom. 2003;72(3):115-127. doi:10.1159/000069738
35. Taliaz D, Serretti A. Investigation of psychoactive medications: challenges and a practical and scalable new path. CNS Neurol Disord Drug Targets. Published online June 28, 2022. doi:10.2174/1871527321666220628103843
36. Gutiérrez-Rojas L, Porras-Segovia A, Dunne H, Andrade-González N, Cervilla JA. Prevalence and correlates of major depressive disorder: a systematic review. Rev Bras Psiquiatr Sao Paulo Braz 1999. 2020;42(6):657-672. doi:10.1590/1516-4446-2020-0650
37. Fried E. Moving forward: how depression heterogeneity hinders progress in treatment and research. Expert Rev Neurother. 2017;17(5):423-425. doi:10.1080/14737175.2017.1307737
38. Zimmerman M, Ellison W, Young D, Chelminski I, Dalrymple K. How many different ways do patients meet the diagnostic criteria for major depressive disorder? Compr Psychiatry. 2015;56:29-34. doi:10.1016/j.comppsych.2014.09.007
39. Sun J, Dong QX, Wang SW, et al. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatry. 2023;87:103705. doi:10.1016/j.ajp.2023.103705
40. Aziz S, Alsaad R, Abd-Alrazaq A, Ahmed A, Sheikh J. Performance of artificial intelligence in predicting future depression levels. Stud Health Technol Inform. 2023;305:452-455. doi:10.3233/SHTI230529
41. Sadeh-Sharvit S, Camp TD, Horton SE, et al. Effects of an artificial intelligence platform for behavioral interventions on depression and anxiety symptoms: randomized clinical trial. J Med Internet Res. 2023;25:e46781. doi:10.2196/46781
42. Shusharina N, Yukhnenko D, Botman S, et al. Modern methods of diagnostics and treatment of neurodegenerative diseases and depression. Diagn Basel Switz. 2023;13(3):573. doi:10.3390/diagnostics13030573
43. Abd-Alrazaq A, AlSaad R, Aziz S, et al. Wearable artificial intelligence for anxiety and depression: scoping review. J Med Internet Res. 2023;25:e42672. doi:10.2196/42672
44. Barua PD, Vicnesh J, Lih OS, et al. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn. Published online November 22, 2022:1-22. doi:10.1007/s11571-022-09904-0
45. Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. Lancet Digit Health. 2022;4(11):e829-e840. doi:10.1016/S2589-7500(22)00153-4
46. Medical Device Coordination Group. MDCG 2019-11. Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 – MDR and Regulation (EU) 2017/746 – IVDR.; 2019. https://health.ec.europa.eu/system/files/2020-09/md_mdcg_2019_11_guidance_qualification_classification_software_en_0.pdf
47. European Commission. MEDDEV 2.1/6. Guidelines on the Qualification and Classification of Stand Alone Software Used in Healthcare within the Regulatory Framework of Medical Devices.; 2016. https://www.medical-device-regulation.eu/wp-content/uploads/2019/05/2_1_6_072016_en.pdf
48. Medical Device Coordination Group. MDCG 2020-7. Post-Market Clinical Follow-up (PMCF) Plan Template. A Guide for Manufacturers and Notified Bodies.; 2020. https://health.ec.europa.eu/system/files/2020-09/md_mdcg_2020_7_guidance_pmcf_plan_template_en_0.pdf
49. Medical Device Coordination Group. MDCG 2022-21. Guidance on Periodic Safety Update Report (PSUR) According to Regulation (EU) 2017/745 (MDR).; 2022. https://health.ec.europa.eu/system/files/2023-01/mdcg_2022-21_en.pdf
50. Van Laere S, Muylle KM, Cornu P. Clinical Decision Support and New Regulatory Frameworks for Medical Devices: Are We Ready for It? - A Viewpoint Paper. Int J Health Policy Manag. 2022;11(12):3159-3163. doi:10.34172/ijhpm.2021.144
51. Food and Drug Administration. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. Guidance for Industry and Food and Drug Administration Staff.; 2017. https://www.fda.gov/media/99447/download
52. Food and Drug Administration. Software as a Medical Device (SAMD): Clinical Evaluation. Guidance for Industry and Food and Drug Administration Staff.; 2017. https://www.fda.gov/media/100714/download
53. Food and Drug Administration. Developing a Software Precertification Program: A Working Model. v1.0.; 2019. https://www.fda.gov/media/119722/download
54. Food and Drug Administration. The Software Precertification (Pre-Cert) Pilot Program: Tailored Total Product Lifecycle Approaches and Key Findings.; 2022. https://www.fda.gov/media/161815/download
55. Food and Drug Administration. Computer Software Assurance for Production and Quality System Software. Draft Guidance for Industry and Food and Drug Administration Staff.; 2022. https://www.fda.gov/media/161521/download
56. Food and Drug Administration. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff.; 2022. https://www.fda.gov/media/109618/download
57. Austin S. Clinical decision support software approach updated in the U.S. but still confused in Europe. Lexology. Published May 25, 2023. Accessed September 4, 2023. https://www.lexology.com/library/detail.aspx?g=de34347d-a834-4d73-a1b2-cd68cb3eecb6
58. McKee M, Wouters OJ. The challenges of regulating artificial intelligence in healthcare Comment on “Clinical decision support and new regulatory frameworks for medical devices: are we ready for it? - a viewpoint paper.” Int J Health Policy Manag. 2023;12:7261. doi:10.34172/ijhpm.2022.7261
59. European Commission. A European approach to artificial intelligence. Published June 19, 2023. Accessed September 20, 2023. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
60. Food and Drug Administration. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD). Department of Health and Human Services (United States); 2019. https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf
61. Food and Drug Administration. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan.; 2021. https://www.fda.gov/media/145022/download
62. Tom M. Mitchell. Machine Learning: A Multistrategy Approach.; 1997.
63. Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence–and machine learning–based software devices in medicine. Jama. 2019;322(23):2285-2286. doi:10.1001/jama.2019.16842
64. Food and Drug Administration. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions. Draft Guidance for Industry and Food and Drug Administration Staff.; 2023. https://www.fda.gov/media/166704/download
65. Kharadi D, Patel K, Rana D, Patel V. Off-label drug use in psychiatry outpatient department: a prospective study at a tertiary care teaching hospital. J Basic Clin Pharm. 2015;6(2):45. doi:10.4103/0976-0105.152090
66. Hálfdánarson Ó, Zoëga H, Aagaard L, et al. International trends in antipsychotic use: A study in 16 countries, 2005–2014. Eur Neuropsychopharmacol. 2017;27(10):1064-1076. doi:10.1016/j.euroneuro.2017.07.001
67. Alexander GC, Gallagher SA, Mascola A, Moloney RM, Stafford RS. Increasing off-label use of antipsychotic medications in the United States, 1995-2008. Pharmacoepidemiol Drug Saf. 2011;20(2):177-184. doi:10.1002/pds.2082
68. Radley DC, Finkelstein SN, Stafford RS. Off-label prescribing among office-based physicians. Arch Intern Med. 2006;166(9):1021-1026. doi:10.1001/archinte.166.9.1021
2. Newson JJ, Hunter D, Thiagarajan TC. The heterogeneity of mental health assessment. Front Psychiatry. 2020;11:76. doi:10.3389/fpsyt.2020.00076
3. Olbert CM, Gala GJ, Tupler LA. Quantifying heterogeneity attributable to polythetic diagnostic criteria: theoretical framework and empirical application. J Abnorm Psychol. 2014;123(2):452. doi:10.1037/a0036068
4. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Pub; 2013:ISBN 978-0-89042-557-2.
5. Perugi G, Barbuti M. There are no patients without comorbidity. Eur Neuropsychopharmacol J Eur Coll Neuropsychopharmacol. 2021;50:104-106. doi:10.1016/j.euroneuro.2021.05.002
6. Loftus J, Scott J, Vorspan F, et al. Psychiatric comorbidities in bipolar disorders: an examination of the prevalence and chronology of onset according to sex and bipolar subtype. J Affect Disord. 2020;267:258-263. doi:10.1016/j.jad.2020.02.035
7. McIntyre RS, Berk M, Brietzke E, et al. Bipolar disorders. The Lancet. 2020;396(10265):1841-1856. doi:10.1016/S0140-6736(20)31544-0
8. Perugi G, Pallucchini A, Rizzato S, Pinzone V, De Rossi P. Current and emerging pharmacotherapy for the treatment of adult attention deficit hyperactivity disorder (ADHD). Expert Opin Pharmacother. 2019;20(12):1457-1470. doi:10.1080/14656566.2019.1618270
9. Butlen-Ducuing F, Haas M, Pani L, van Zwieten-Boot B, Broich K. DSM-5 and clinical trials in psychiatry: challenges to come? Nat Rev Drug Discov. 2012;11(8):583-584. doi:10.1038/nrd3811
10. Widiger TA. Categorical versus dimensional classification: Implications from and for research. J Personal Disord. 1992;6(4):287. doi:10.1521/pedi.1992.6.4.287
11. Guze SB. Nature of psychiatric illness: Why psychiatry is a branch of medicine. Compr Psychiatry. 1978;19(4):295-307. doi:10.1016/0010-440x(78)90012-3
12. Widiger TA, Trull TJ. Plate tectonics in the classification of personality disorder: shifting to a dimensional model. Am Psychol. 2007;62(2):71. doi:10.1037/0003-066X.62.2.71
13. Livesley WJ, Jackson DN, Schroeder ML. Factorial structure of traits delineating personality disorders in clinical and general population samples. J Abnorm Psychol. 1992;101(3):432. doi:10.1037//0021-843x.101.3.432
14. Goldberg D. A dimensional model for common mental disorders. Br J Psychiatry. 1996;168(S30):44-49.
15. Taliaz D, Souery D. A new characterization of mental health disorders using digital behavioral data: evidence from Major Depressive Disorder. J Clin Med. 2021;10(14):3109. doi:10.3390/jcm10143109
16. Jones C, Thornton J, Wyatt JC. Enhancing trust in clinical decision support systems: a framework for developers. BMJ Health Care Inform. 2021;28(1). doi:10.1136/bmjhci-2020-100247
17. Harris MG, Kazdin AE, Chiu WT, et al. Findings from world mental health surveys of the perceived helpfulness of treatment for patients with Major Depressive Disorder. JAMA Psychiatry. 2020;77(8):830-841. doi:10.1001/jamapsychiatry.2020.1107
18. van Westrhenen R, Ingelman-Sundberg M. Editorial: from trial and error to individualised pharmacogenomics-based pharmacotherapy in psychiatry. Front Pharmacol. 2021;12:725565. doi:10.3389/fphar.2021.725565
19. Suzuki K, Chen Y. Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Vol 140. Springer; 2018.
20. Shen J, Zhang CJ, Jiang B, et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Inform. 2019;7(3):e10010. doi:10.2196/10010
21. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056
22. Han SS, Park GH, Lim W, et al. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PloS One. 2018;13(1):e0191493. doi:10.1371/journal.pone.0191493
23. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. doi:10.1136/svn-2017-000101
24. Szolovits P. Artificial intelligence and medicine. In: Artificial Intelligence in Medicine. Routledge; 2019:1-19.
25. D’alfonso S, Santesteban-Echarri O, Rice S, et al. Artificial intelligence-assisted online social therapy for youth mental health. Front Psychol. 2017;8:796. doi:10.3389/fpsyg.2017.00796
26. Bedi G, Carrillo F, Cecchi GA, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophr. 2015;1(1):1-7. doi:10.1038/npjschz.2015.30
27. Poulin C, Shiner B, Thompson P, et al. Predicting the risk of suicide by analyzing the text of clinical notes. PloS One. 2014;9(1):e85733. doi:10.1371/journal.pone.0085733
28. Taliaz D, Spinrad A, Barzilay R, et al. Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry. 2021;11(1):1-9. doi:10.1038/s41398-021-01488-3
29. Fakhoury M. Artificial intelligence in psychiatry. Adv Exp Med Biol. 2019;1192:119-125. doi:10.1007/978-981-32-9721-0_6
30. Center for Devices and Radiological Health. General/Specific Intended Use - Guidance for Industry. U.S. Food and Drug Administration. Published March 18, 2020. Accessed October 23, 2022. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/generalspecific-intended-use-guidance-industry
31. The European Parliament and the Council of the European Union. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on Medical Devices, Amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and Repealing Council Directives 90/385/EEC and 93/42/EEC.; 2017. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0745
32. Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617-627. doi:10.1001/archpsyc.62.6.617
33. Walsh BT, Seidman SN, Sysko R, Gould M. Placebo response in studies of major depression: variable, substantial, and growing. JAMA. 2002;287(14):1840-1847. doi:10.1001/jama.287.14.1840
34. Fava M, Evins AE, Dorer DJ, Schoenfeld DA. The problem of the placebo response in clinical trials for psychiatric disorders: culprits, possible remedies, and a novel study design approach. Psychother Psychosom. 2003;72(3):115-127. doi:10.1159/000069738
35. Taliaz D, Serretti A. Investigation of psychoactive medications: challenges and a practical and scalable new path. CNS Neurol Disord Drug Targets. Published online June 28, 2022. doi:10.2174/1871527321666220628103843
36. Gutiérrez-Rojas L, Porras-Segovia A, Dunne H, Andrade-González N, Cervilla JA. Prevalence and correlates of major depressive disorder: a systematic review. Rev Bras Psiquiatr Sao Paulo Braz 1999. 2020;42(6):657-672. doi:10.1590/1516-4446-2020-0650
37. Fried E. Moving forward: how depression heterogeneity hinders progress in treatment and research. Expert Rev Neurother. 2017;17(5):423-425. doi:10.1080/14737175.2017.1307737
38. Zimmerman M, Ellison W, Young D, Chelminski I, Dalrymple K. How many different ways do patients meet the diagnostic criteria for major depressive disorder? Compr Psychiatry. 2015;56:29-34. doi:10.1016/j.comppsych.2014.09.007
39. Sun J, Dong QX, Wang SW, et al. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatry. 2023;87:103705. doi:10.1016/j.ajp.2023.103705
40. Aziz S, Alsaad R, Abd-Alrazaq A, Ahmed A, Sheikh J. Performance of artificial intelligence in predicting future depression levels. Stud Health Technol Inform. 2023;305:452-455. doi:10.3233/SHTI230529
41. Sadeh-Sharvit S, Camp TD, Horton SE, et al. Effects of an artificial intelligence platform for behavioral interventions on depression and anxiety symptoms: randomized clinical trial. J Med Internet Res. 2023;25:e46781. doi:10.2196/46781
42. Shusharina N, Yukhnenko D, Botman S, et al. Modern methods of diagnostics and treatment of neurodegenerative diseases and depression. Diagn Basel Switz. 2023;13(3):573. doi:10.3390/diagnostics13030573
43. Abd-Alrazaq A, AlSaad R, Aziz S, et al. Wearable artificial intelligence for anxiety and depression: scoping review. J Med Internet Res. 2023;25:e42672. doi:10.2196/42672
44. Barua PD, Vicnesh J, Lih OS, et al. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn. Published online November 22, 2022:1-22. doi:10.1007/s11571-022-09904-0
45. Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. Lancet Digit Health. 2022;4(11):e829-e840. doi:10.1016/S2589-7500(22)00153-4
46. Medical Device Coordination Group. MDCG 2019-11. Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 – MDR and Regulation (EU) 2017/746 – IVDR.; 2019. https://health.ec.europa.eu/system/files/2020-09/md_mdcg_2019_11_guidance_qualification_classification_software_en_0.pdf
47. European Commission. MEDDEV 2.1/6. Guidelines on the Qualification and Classification of Stand Alone Software Used in Healthcare within the Regulatory Framework of Medical Devices.; 2016. https://www.medical-device-regulation.eu/wp-content/uploads/2019/05/2_1_6_072016_en.pdf
48. Medical Device Coordination Group. MDCG 2020-7. Post-Market Clinical Follow-up (PMCF) Plan Template. A Guide for Manufacturers and Notified Bodies.; 2020. https://health.ec.europa.eu/system/files/2020-09/md_mdcg_2020_7_guidance_pmcf_plan_template_en_0.pdf
49. Medical Device Coordination Group. MDCG 2022-21. Guidance on Periodic Safety Update Report (PSUR) According to Regulation (EU) 2017/745 (MDR).; 2022. https://health.ec.europa.eu/system/files/2023-01/mdcg_2022-21_en.pdf
50. Van Laere S, Muylle KM, Cornu P. Clinical Decision Support and New Regulatory Frameworks for Medical Devices: Are We Ready for It? - A Viewpoint Paper. Int J Health Policy Manag. 2022;11(12):3159-3163. doi:10.34172/ijhpm.2021.144
51. Food and Drug Administration. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. Guidance for Industry and Food and Drug Administration Staff.; 2017. https://www.fda.gov/media/99447/download
52. Food and Drug Administration. Software as a Medical Device (SAMD): Clinical Evaluation. Guidance for Industry and Food and Drug Administration Staff.; 2017. https://www.fda.gov/media/100714/download
53. Food and Drug Administration. Developing a Software Precertification Program: A Working Model. v1.0.; 2019. https://www.fda.gov/media/119722/download
54. Food and Drug Administration. The Software Precertification (Pre-Cert) Pilot Program: Tailored Total Product Lifecycle Approaches and Key Findings.; 2022. https://www.fda.gov/media/161815/download
55. Food and Drug Administration. Computer Software Assurance for Production and Quality System Software. Draft Guidance for Industry and Food and Drug Administration Staff.; 2022. https://www.fda.gov/media/161521/download
56. Food and Drug Administration. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff.; 2022. https://www.fda.gov/media/109618/download
57. Austin S. Clinical decision support software approach updated in the U.S. but still confused in Europe. Lexology. Published May 25, 2023. Accessed September 4, 2023. https://www.lexology.com/library/detail.aspx?g=de34347d-a834-4d73-a1b2-cd68cb3eecb6
58. McKee M, Wouters OJ. The challenges of regulating artificial intelligence in healthcare Comment on “Clinical decision support and new regulatory frameworks for medical devices: are we ready for it? - a viewpoint paper.” Int J Health Policy Manag. 2023;12:7261. doi:10.34172/ijhpm.2022.7261
59. European Commission. A European approach to artificial intelligence. Published June 19, 2023. Accessed September 20, 2023. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
60. Food and Drug Administration. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD). Department of Health and Human Services (United States); 2019. https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf
61. Food and Drug Administration. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan.; 2021. https://www.fda.gov/media/145022/download
62. Tom M. Mitchell. Machine Learning: A Multistrategy Approach.; 1997.
63. Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence–and machine learning–based software devices in medicine. Jama. 2019;322(23):2285-2286. doi:10.1001/jama.2019.16842
64. Food and Drug Administration. Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions. Draft Guidance for Industry and Food and Drug Administration Staff.; 2023. https://www.fda.gov/media/166704/download
65. Kharadi D, Patel K, Rana D, Patel V. Off-label drug use in psychiatry outpatient department: a prospective study at a tertiary care teaching hospital. J Basic Clin Pharm. 2015;6(2):45. doi:10.4103/0976-0105.152090
66. Hálfdánarson Ó, Zoëga H, Aagaard L, et al. International trends in antipsychotic use: A study in 16 countries, 2005–2014. Eur Neuropsychopharmacol. 2017;27(10):1064-1076. doi:10.1016/j.euroneuro.2017.07.001
67. Alexander GC, Gallagher SA, Mascola A, Moloney RM, Stafford RS. Increasing off-label use of antipsychotic medications in the United States, 1995-2008. Pharmacoepidemiol Drug Saf. 2011;20(2):177-184. doi:10.1002/pds.2082
68. Radley DC, Finkelstein SN, Stafford RS. Off-label prescribing among office-based physicians. Arch Intern Med. 2006;166(9):1021-1026. doi:10.1001/archinte.166.9.1021