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Cited by 97Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence in Healthcare

Evidence, ethics and the promise of artificial intelligence in psychiatry

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McCradden, Melissa, Hui, Katrina, Buchman, Daniel Z·2023·Journal of Medical Ethics
DOI 10.1136/jme-2022-108447PMC PMC10423547
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Abstract

Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine’s understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry.

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  • Prior work in clinical decision support and mental health law has emphasized the limitations of purely statistical or black-box models in capturing the ambiguity of psychiatric documentation and judicial interpretation.

    “Prior work in clinical decision support and mental health law has emphasized the limitations of purely statistical or black-box models in capturing the ambiguity of psychiatric documentation and judicial interpretation (Chekroud et al., 2021; McCradden et al., 2023).”

    From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as McCradden et al., 2023

    The ‘EBM, clinical judgement and AI’ section (page unavailable) says that ambiguity in psychiatric classifications makes reliance on ML predictions tenuous. The paper also discusses biased descriptors in electronic health records, but it does not address models capturing ambiguity in psychiatric documentation or judicial interpretation under mental health law. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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  1. [1]Monteith S, Glenn T, Geddes J, et al. Expectations for artificial intelligence (AI) in psychiatry. Curr Psychiatry Rep 2022; 24(11): 709– 21. 10.1007/s11920-022-01378-5 36214931 PMC9549456 · DOI
  2. [2]Graham S, Depp C, Lee EE, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep 2019; 21(11): 116. 10.1007/s11920-019-1094-0 31701320 PMC7274446 · DOI · Paper page
  3. [3]Lee EE, Torous J, De Choudhury M, et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging 2021; 6(9). 10.1016/j.bpsc.2021.02.001 PMC8349367 33571718 · DOI
  4. [4]Bayramli I, Castro V, Barak-Corren Y, et al. Predictive structured-unstructured interactions in EHR models: a case study of suicide prediction. NPJ Digit Med 2022; 5(1). 10.1038/s41746-022-00558-0 PMC8795240 35087182 · DOI
  5. [5]Davis JE. 'The explanation you have been looking for': neurobiology as promise and hermeneutic closure. Cult Med Psychiatry 2022; 46(1): 76– 100. 10.1007/s11013-021-09737-2 34351549 · DOI
  6. [6]Scull A. American psychiatry in the new millennium: a critical appraisal. Psychol Med 2021; 51(16): 2762– 70. 10.1017/S0033291721001975 34158141 · DOI
  7. [7]Rose N. Neuroscience and the future for mental health? Epidemiol Psychiatr Sci 2016; 25(2): 95– 100. 10.1017/S2045796015000621 26234570 PMC6998666 · DOI
  8. [8]Lane R. Expanding boundaries in psychiatry: uncertainty in the context of diagnosis-seeking and negotiation. Sociol Health Illn 2020; 42 Suppl 1(Suppl 1): 69– 83. 10.1111/1467-9566.13044 31849066 PMC7496635 · DOI
  9. [9]Hautamäki L. Uncertainty work and temporality in psychiatry: how clinicians and patients experience and manage risk in practice? Health Risk Soc 2018; 20(1-2): 43– 62. 10.1080/13698575.2018.1442918 · DOI
  10. [10]Fox R. Medical uncertainty revisited. In: Albrecht G, Fitzpatrick R, Scrimshaw S, eds. Handbook of social studies in health and medicine. London: Sage Publications, 2000.
  11. [11]Lane N, Broome M. Towards personalised predictive psychiatry in clinical practice: an ethical perspective. Br J Psychiatry 2022: 172– 4. 10.1192/bjp.2022.37 35249567 · DOI
  12. [13]Cunningham PJ. Beyond parity: primary care physicians' perspectives on access to mental health care. Health Aff 2009; 28(3): w490– 501. 10.1377/hlthaff.28.3.w490 19366722 · DOI · Paper page
  13. [14]Gratzer D, Goldbloom D, Therapy GD. Therapy and E-therapy-preparing future psychiatrists in the era of apps and chatbots. Acad Psychiatry 2020; 44(2): 231– 4. 10.1007/s40596-019-01170-3 31898301 · DOI
  14. [15]Sedlakova J, Trachsel M. Conversational artificial intelligence in psychotherapy: a new therapeutic tool or agent? Am J Bioeth 2022: 1– 10. 10.1080/15265161.2022.2048739 35362368 · DOI
  15. [16]Zhou Q, Chen Z-H, Cao Y-H, et al. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit Med 2021; 4(1). 10.1038/s41746-021-00524-2 PMC8553754 34711955 · DOI · Paper page
  16. [17]Babic B, Gerke S, Evgeniou T, et al. Beware explanations from AI in health care. Science 2021; 373(6552): 284– 6. 10.1126/science.abg1834 34437144 · DOI · Paper page
  17. [20]Ho A. Artificial intelligence as a feminist bioethics issue. In: Rogers WA, Scully JL, Carter SM, et al, eds. The Routledge Handbook of feminist bioethics. New York: Routledge, 2022: 291– 307.
  18. [21]Goldenberg MJ. On evidence and evidence-based medicine: lessons from the philosophy of science. Soc Sci Med 2006; 62(11): 2621– 32. 10.1016/j.socscimed.2005.11.031 16384628 · DOI
  19. [24]Haynes RB, Devereaux PJ, Guyatt GH. Clinical expertise in the era of evidence-based medicine and patient choice. ACP J Club 2002; 136(2): A11– 14. 10.7326/ACPJC-2002-136-2-A11 11874303 · DOI
  20. [25]Kelly MP, Heath I, Howick J, et al. The importance of values in evidence-based medicine. BMC Med Ethics 2015; 16(1): 69. 10.1186/s12910-015-0063-3 26459219 PMC4603687 · DOI
  21. [27]Singh I, Rose N. Biomarkers in psychiatry. Nature 2009; 460(7252): 202– 7. 10.1038/460202a 19587761 · DOI
  22. [29]Birhane A. The impossibility of automating ambiguity. Artif Life 2021; 27(1): 44– 61. 10.1162/artl_a_00336 34529757 · DOI
  23. [30]Chin-Yee B, Upshur R. Three problems with big data and artificial intelligence in medicine. Perspect Biol Med 2019; 62(2): 237– 56. 10.1353/pbm.2019.0012 31281120 · DOI
  24. [31]Cohen IG, Amarasingham R, Shah A, et al. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff 2014; 33(7): 1139– 47. 10.1377/hlthaff.2014.0048 25006139 · DOI · Paper page
  25. [32]Chin-Yee B, Upshur R. Clinical judgement in the era of big data and predictive analytics. J Eval Clin Pract 2018; 24(3): 638– 45. 10.1111/jep.12852 29237237 · DOI · Paper page
  26. [33]Schwab A. Epistemic humility and medical practice: translating epistemic categories into ethical obligations. J Med Philos 2012; 37(1): 28– 48. 10.1093/jmp/jhr054 22241866 · DOI
  27. [34]Gerhards H, Weber K, Bittner U, et al. Machine learning healthcare applications (ML-HCAs) are no stand-alone systems but part of an Ecosystem - A broader ethical and health technology assessment approach is needed. Am J Bioeth 2020; 20(11): 46– 8. 10.1080/15265161.2020.1820104 33103972 · DOI
  28. [36]Triberti S, Durosini I, Pravettoni G. A "third wheel" effect in health decision making involving artificial entities: a psychological perspective. Front Public Health 2020; 8: 117. 10.3389/fpubh.2020.00117 32411641 PMC7199477 · DOI
  29. [37]Abbasgholizadeh Rahimi S, Cwintal M, Huang Y, et al. Application of artificial intelligence in shared decision making: scoping review. JMIR Med Inform 2022; 10(8): e36199. 10.2196/36199 35943793 PMC9399841 · DOI
  30. [38]Elwyn G, Laitner S, Coulter A, et al. Implementing shared decision making in the NHS. BMJ 2010; 341: c5146. 10.1136/bmj.c5146 20947577 · DOI
  31. [39]Kunneman M, Montori VM. When patient-centred care is worth doing well: informed consent or shared decision-making. BMJ Qual Saf 2017; 26(7): 522– 4. 10.1136/bmjqs-2016-005969 27672122 · DOI
  32. [41]Brown C, Story GW, Mourão-Miranda J, et al. Will artificial intelligence eventually replace psychiatrists? Br J Psychiatry 2021; 218(3): 131– 4. 10.1192/bjp.2019.245 31806072 · DOI
  33. [42]Roessner V, Rothe J, Kohls G, et al. Taming the chaos?! using eXplainable artificial intelligence (XaI) to tackle the complexity in mental health research. Eur Child Adolesc Psychiatry 2021; 30(8): 1143– 6. 10.1007/s00787-021-01836-0 34240264 PMC8310842 · DOI
  34. [45]London AJ. Artificial intelligence and black-box medical decisions: accuracy versus Explainability. Hastings Cent Rep 2019; 49(1): 15– 21. 10.1002/hast.973 30790315 · DOI · Paper page
  35. [47]McCradden MD, Anderson JA, A. Stephenson E, et al. A research ethics framework for the clinical translation of healthcare machine learning. The American Journal of Bioethics 2022; 22(5): 8– 22. 10.1080/15265161.2021.2013977 35048782 · DOI
  36. [48]Tomsett R, Harborne D, Chakraborty S. Proceedings of the AAAI Conference on Artificial Intelligence. In: Sanity checks for Saliency metrics. 30, 2020: 6021– 9.
  37. [49]Rajpurkar P, Irvin J, Zhu K. CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. arxiv logo 2017. 10.48550/arXiv.1711.05225 · DOI
  38. [50]Constantino MJ, Boswell JF, Coyne AE, et al. Effect of matching therapists to patients vs assignment as usual on adult psychotherapy outcomes: a randomized clinical trial. JAMA Psychiatry 2021; 78(9): 960– 9. 10.1001/jamapsychiatry.2021.1221 34106240 PMC8190692 · DOI · Paper page
  39. [51]Tonekaboni S, Joshi S, McCradden MD. What clinicians want: Contextualizing explainable machine learning for clinical end use. In: Proceedings of machine learning research, 2019. http://proceedings.mlr.press/v106/tonekaboni19a/tonekaboni19a.pdf
  40. [54]Buçinca Z, Lin P, Gajos KZ, et al. Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems. Proceedings of the 25th International Conference on Intelligent User Interfaces, 2020.
  41. [56]Tschandl P, Rinner C, Apalla Z, et al. Human-computer collaboration for skin cancer recognition. Nat Med 2020; 26(8): 1229– 34. 10.1038/s41591-020-0942-0 32572267 · DOI · Paper page
  42. [58]Gupta M. Does evidence-based medicine apply to psychiatry? Theor Med Bioeth 2007; 28(2): 103– 20. 10.1007/s11017-007-9029-x 17503219 · DOI
  43. [59]Ho A. Trusting experts and epistemic humility in disability. IJFAB 2011; 4(2): 102– 23. 10.3138/ijfab.4.2.102 · DOI
  44. [60]Boys JA, Alicuben ET, DeMeester MJ, et al. Public perceptions on robotic surgery, hospitals with robots, and surgeons that use them. Surg Endosc 2016; 30(4): 1310– 6. 10.1007/s00464-015-4368-6 26173543 · DOI
  45. [62]McCradden MD. When is accuracy off-target? Transl Psychiatry 2021; 11(1): 369. 10.1038/s41398-021-01479-4 34226492 PMC8257647 · DOI
  46. [63]Jacobs M, He J, Pradier MF. Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. In: Proceedings of the 2021 chi conference on human factors in computing systems, 2021.
  47. [65]Carel H, Kidd IJ. Epistemic Injustice in Medicine and Healthcare. In: The Routledge Handbook of Epistemic injustice, 2017: 336– 46.
  48. [66]Scrutton AP, Kidd IJ, Medina IJ, et al. Epistemic Injustice and mental illness. In: Kidd IJ, Medina IJ, Pohlhaus IJ, eds. The Routledge Handbook of Epistemic injustice. Routledge, 2017: 347– 55.
  49. [67]Newbigging K, Ridley J. Epistemic struggles: the role of advocacy in promoting epistemic justice and rights in mental health. Soc Sci Med 2018; 219: 36– 44. 10.1016/j.socscimed.2018.10.003 30359905 · DOI
  50. [68]Sun M, Oliwa T, Peek ME, et al. Negative patient descriptors: documenting racial bias in the electronic health record. Health Aff 2022; 41(2): 203– 11. 10.1377/hlthaff.2021.01423 PMC8973827 35044842 · DOI · Paper page
  51. [69]Noble SU. Algorithms of Oppression: how search engines reinforce racism. New York: New York University Press, 2018. 34709921
  52. [70]Lee EK, Donley G, Ciesielski TH, et al. Health outcomes in redlined versus non-redlined neighborhoods: a systematic review and meta-analysis. Soc Sci Med 2022; 294: 114696. 10.1016/j.socscimed.2021.114696 34995988 · DOI
  53. [71]Guta A, Voronka J, Gagnon M. Resisting the digital medicine panopticon: toward a bioethics of the oppressed. Am J Bioeth 2018; 18(9): 62– 4. 10.1080/15265161.2018.1498936 30235095 · DOI
  54. [72]Daston L, Galison P. The image of objectivity. Representations 1992; 40: 81– 128. 10.2307/2928741 · DOI · Paper page
  55. [73]Carel H, Kidd IJ. Epistemic injustice in healthcare: a philosophial analysis. Med Health Care Philos 2014; 17(4): 529– 40. 10.1007/s11019-014-9560-2 24740808 · DOI · Paper page
  56. [74]Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019; 17(1): 195. 10.1186/s12916-019-1426-2 31665002 PMC6821018 · DOI · Paper page
  57. [75]Minsky S, Vega W, Miskimen T, et al. Diagnostic patterns in Latino, African American, and European American psychiatric patients. Arch Gen Psychiatry 2003; 60(6): 637– 44. 10.1001/archpsyc.60.6.637 12796227 · DOI
  58. [76]Uusitalo S, Tuominen J, Arstila V. Mapping out the philosophical questions of AI and clinical practice in diagnosing and treating mental disorders. J Eval Clin Pract 2021; 27(3): 478– 84. 10.1111/jep.13485 32996664 · DOI
  59. [79]Benedetti F, Mayberg HS, Wager TD, et al. Neurobiological mechanisms of the placebo effect. J Neurosci 2005; 25(45): 10390– 402. 10.1523/JNEUROSCI.3458-05.2005 16280578 PMC6725834 · DOI
  60. [80]Finset A. 50 years of research on the effect of physician communication behavior on health outcomes. Patient Educ Couns 2014; 96(1): 1– 2. 10.1016/j.pec.2014.05.018 24931942 · DOI
  61. [81]Parnas S, Isobel S. Navigating the social synapse: the neurobiology of bedside manner. Australas Psychiatry 2018; 26(1): 70– 2. 10.1177/1039856217726224 28836828 · DOI

Cited by

Sorted by citations

Authors
McCradden, M., Hui, K., & Buchman, D. Z.
Affiliations
  • University of Toronto (CA)
  • Centre for Addiction and Mental Health (CA)
Corresponding author
Buchman, Daniel Z
ORCID
Venue
Journal of Medical Ethics
Publisher
BMJ
Published
2022-12-29
Volume
49
Issue
8
Pages
573-579
ISSN
  • 0306-6800 (ISSN-L)
  • 1473-4257
DOI
10.1136/jme-2022-108447
PMC
PMC10423547
OpenAlex
W4313289426
Cited by
97
References
15
Citations by year
2026: 23 · 2025: 32 · 2024: 32 · 2023: 10
FWCI
5.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Medical Ethics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • Associated Medical Services (Fellowship in Compassion and AI (N/A))
  • Dalla Lana School of Public Health (Data Science Interdisciplinary Seed Funding (N/A))
  • Campbell Family Mental Health Researc​h Institute (Discovery Fund (N/A), Koerner Research Scholar (N/A))
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence in psychiatry, epistemic humility, clinical decision-making, psychiatric diagnosis, prognosis, precision medicine, digital twin, predictive analytics, big data, patient rights, evidence-based medicine
MeSH terms
Clinical Decision-Making, Artificial Intelligence, Mental Disorders, Humans, Psychiatry, Precision Medicine

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