Ethical Considerations in Neuroprognostication Following Acute Brain Injury

India A. Lissak; Brian L. Edlow; Eric Rosenthal; Michael J. Young

doi:10.1055/s-0043-1775597

Seminars in Neurology, Table of Contents

Semin Neurol 2023; 43(05): 758-767
DOI: 10.1055/s-0043-1775597

Review Article

Ethical Considerations in Neuroprognostication Following Acute Brain Injury

India A. Lissak

¹Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

,

Brian L. Edlow

¹Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts

,

Eric Rosenthal

¹Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

,

Michael J. Young

¹Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

› Author Affiliations

Abstract

Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrating vast amounts of information to predict a patient's likely trajectory of neurologic recovery. In this setting, critically evaluating salient ethical questions is imperative, and the implications often inform high-stakes conversations about the continuation, limitation, or withdrawal of life-sustaining therapy. While neuroprognostication is central to these clinical “life-or-death” decisions, the ethical underpinnings of neuroprognostication itself have been underexplored for patients with ABI. In this article, we discuss the ethical challenges of individualized neuroprognostication including parsing and communicating its inherent uncertainty to surrogate decision-makers. We also explore the population-based ethical considerations that arise in the context of heterogenous prognostication practices. Finally, we examine the emergence of artificial intelligence-aided neuroprognostication, proposing an ethical framework relevant to both modern and longstanding prognostic tools.

Keywords

Neuroprognostication - brain injury - disorders of consciousness - neuroethics - coma - artificial intelligence

Full Text

References

References
1 Fischer D, Edlow BL, Giacino JT, Greer DM. Neuroprognostication: a conceptual framework. Nat Rev Neurol 2022; 18 (07) 419-427
2 Searle JR. Austin on locutionary and illocutionary acts. Philos Rev 1968; 77 (04) 405-424
3 Austin JL, Sbisa M, Urmson JO, Nidditch PH. How to Do Things with Words. Oxford: Oxford University Press; 1980
4 Maciel CB, Barden MM, Youn TS, Dhakar MB, Greer DM. Neuroprognostication practices in postcardiac arrest patients: an international survey of critical care providers. Crit Care Med 2020; 48 (02) e107-e114
5 Hunfeld M, Muusers MAC, Catsman CE, Castillo JD, Tibboel D, Buysse CMP. The current practice regarding neuro-prognostication for comatose children after cardiac arrest differs between and within European PICUs: a survey. Eur J Paediatr Neurol 2020; 28: 44-51
6 Robertsen A, Førde R, Skaga NO, Helseth E. Treatment-limiting decisions in patients with severe traumatic brain injury in a Norwegian regional trauma center. Scand J Trauma Resusc Emerg Med 2017; 25 (01) 44
7 Pratt AK, Chang JJ, Sederstrom NO. A fate worse than death: prognostication of devastating brain injury. Crit Care Med 2019; 47 (04) 591-598
8 Weimer JM, Nowacki AS, Frontera JA. Withdrawal of life-sustaining therapy in patients with intracranial hemorrhage: self-fulfilling prophecy or accurate prediction of outcome?. Crit Care Med 2016; 44 (06) 1161-1172
9 Izzy S, Compton R, Carandang R, Hall W, Muehlschlegel S. Self-fulfilling prophecies through withdrawal of care: do they exist in traumatic brain injury, too?. Neurocrit Care 2013; 19 (03) 347-363
10 Elmer J, Torres C, Aufderheide TP. et al; Resuscitation Outcomes Consortium. Association of early withdrawal of life-sustaining therapy for perceived neurological prognosis with mortality after cardiac arrest. Resuscitation 2016; 102: 127-135
11 Turgeon AF, Lauzier F, Burns KE. et al; Canadian Critical Care Trials Group. Determination of neurologic prognosis and clinical decision making in adult patients with severe traumatic brain injury: a survey of Canadian intensivists, neurosurgeons, and neurologists. Crit Care Med 2013; 41 (04) 1086-1093
12 Turgeon AF, Lauzier F, Simard JF. et al; Canadian Critical Care Trials Group. Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: a Canadian multicentre cohort study. CMAJ 2011; 183 (14) 1581-1588
13 Becker KJ, Baxter AB, Cohen WA. et al. Withdrawal of support in intracerebral hemorrhage may lead to self-fulfilling prophecies. Neurology 2001; 56 (06) 766-772
14 Leblanc G, Boutin A, Shemilt M. et al. Incidence and impact of withdrawal of life-sustaining therapies in clinical trials of severe traumatic brain injury: a systematic review. Clin Trials 2018; 15 (04) 398-412
15 Williamson T, Ryser MD, Ubel PA. et al. Withdrawal of life-supporting treatment in severe traumatic brain injury. JAMA Surg 2020; 155 (08) 723-731
16 McCredie VA, Alali AS, Xiong W. et al. Timing of withdrawal of life-sustaining therapies in severe traumatic brain injury: Impact on overall mortality. J Trauma Acute Care Surg 2016; 80 (03) 484-491
17 van Veen E, van der Jagt M, Citerio G. et al; CENTER-TBI investigators and participants. End-of-life practices in traumatic brain injury patients: report of a questionnaire from the CENTER-TBI study. J Crit Care 2020; 58: 78-88
18 Avidan A, Sprung CL, Schefold JC. et al; ETHICUS-2 Study Group. Variations in end-of-life practices in intensive care units worldwide (Ethicus-2): a prospective observational study. Lancet Respir Med 2021; 9 (10) 1101-1110
19 Covelli V, Sattin D, Giovannetti AM, Scaratti C, Willems M, Leonardi M. Caregiver's burden in disorders of consciousness: a longitudinal study. Acta Neurol Scand 2016; 134 (05) 352-359
20 Schnakers C, Chatelle C, Demertzi A, Majerus S, Laureys S. What about pain in disorders of consciousness?. AAPS J 2012; 14 (03) 437-444
21 Moretta P, Estraneo A, De Lucia L, Cardinale V, Loreto V, Trojano L. A study of the psychological distress in family caregivers of patients with prolonged disorders of consciousness during in-hospital rehabilitation. Clin Rehabil 2014; 28 (07) 717-725
22 Olson DM, Hemphill JC, Provencio JJ. et al; Curing Coma Campaign Collaborators. The curing coma campaign and the future of coma research. Semin Neurol 2022; 42 (03) 393-402
23 Kriegel U. The value of consciousness. Analysis 2019; 79 (03) 503-520
24 Edlow BL, Sanz LRD, Polizzotto L. et al; Curing Coma Campaign and its contributing members. Therapies to restore consciousness in patients with severe brain injuries: a gap analysis and future directions. Neurocrit Care 2021; 35 (Suppl. 01) 68-85
25 Edlow BL, Claassen J, Schiff ND, Greer DM. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat Rev Neurol 2021; 17 (03) 135-156
26 Barra A, Monti M, Thibaut A. Noninvasive brain stimulation therapies to promote recovery of consciousness: where we are and where we should go. Semin Neurol 2022; 42 (03) 348-362
27 Fins JJ. The ethics of measuring and modulating consciousness: the imperative of minding time. Prog Brain Res 2009; 177: 371-382
28 Hansson O. Biomarkers for neurodegenerative diseases. Nat Med 2021; 27 (06) 954-963
29 Marshall JC, Reinhart K. International Sepsis Forum. Biomarkers of sepsis. Crit Care Med 2009; 37 (07) 2290-2298
30 Giacoppo S, Bramanti P, Barresi M. et al. Predictive biomarkers of recovery in traumatic brain injury. Neurocrit Care 2012; 16 (03) 470-477
31 Simats A, García-Berrocoso T, Montaner J. Neuroinflammatory biomarkers: from stroke diagnosis and prognosis to therapy. Biochim Biophys Acta 2016; 1862 (03) 411-424
32 Boyd LA, Hayward KS, Ward NS. et al. Biomarkers of stroke recovery: consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable. Int J Stroke 2017; 12 (05) 480-493
33 Wang CH, Chang WT, Su KI. et al. Neuroprognostic accuracy of blood biomarkers for post-cardiac arrest patients: a systematic review and meta-analysis. Resuscitation 2020; 148: 108-117
34 Chennu S, Annen J, Wannez S. et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain 2017; 140 (08) 2120-2132
35 Fleming V, Muehlschlegel S. Neuroprognostication. Crit Care Clin 2023; 39 (01) 139-152
36 Toni D, Fiorelli M, Gentile M. et al. Progressing neurological deficit secondary to acute ischemic stroke. A study on predictability, pathogenesis, and prognosis. Arch Neurol 1995; 52 (07) 670-675
37 Sumer M, Ozdemir I, Erturk O. Progression in acute ischemic stroke: frequency, risk factors and prognosis. J Clin Neurosci 2003; 10 (02) 177-180
38 Nudo RJ. Recovery after brain injury: mechanisms and principles. Front Hum Neurosci 2013; 7: 887
39 Threlkeld ZD, Bodien YG, Rosenthal ES. et al. Functional networks reemerge during recovery of consciousness after acute severe traumatic brain injury. Cortex 2018; 106: 299-308
40 Thengone DJ, Voss HU, Fridman EA, Schiff ND. Local changes in network structure contribute to late communication recovery after severe brain injury. Sci Transl Med 2016; 8 (368) 368re5
41 Katz DI, Polyak M, Coughlan D, Nichols M, Roche A. Natural history of recovery from brain injury after prolonged disorders of consciousness: outcome of patients admitted to inpatient rehabilitation with 1-4 year follow-up. Prog Brain Res 2009; 177: 73-88
42 Hammond FM, Giacino JT, Nakase Richardson R. et al. Disorders of consciousness due to traumatic brain injury: functional status ten years post-injury. J Neurotrauma 2019; 36 (07) 1136-1146
43 Zabihi M, Rubin DB, Ack SE. et al. Resting-state electroencephalography for continuous, passive prediction of coma recovery after acute brain injury. bioRxiv 2022; 2022.09.30.510334
44 Egbebike J, Shen Q, Doyle K. et al. Cognitive-motor dissociation and time to functional recovery in patients with acute brain injury in the USA: a prospective observational cohort study. Lancet Neurol 2022; 21 (08) 704-713
45 Peterson A, Young MJ, Fins JJ. Ethics and the 2018 Practice Guideline on Disorders of Consciousness: a framework for responsible implementation. Neurology 2022; 98 (17) 712-718
46 Souter MJ, Blissitt PA, Blosser S. et al. Recommendations for the critical care management of devastating brain injury: prognostication, psychosocial, and ethical management : a position statement for healthcare professionals from the Neurocritical Care Society. Neurocrit Care 2015; 23 (01) 4-13
47 Perman SM, Kirkpatrick JN, Reitsma AM. et al. Timing of neuroprognostication in postcardiac arrest therapeutic hypothermia*. Crit Care Med 2012; 40 (03) 719-724
48 Fins JJ. Rights Come to Mind: Brain Injury, Ethics, and the Struggle for Consciousness. Cambridge: Cambridge University Press; 2015
49 Giacino JT, Katz DI, Schiff ND. et al. Practice guideline update recommendations summary: disorders of consciousness: report of the guideline development, dissemination, and implementation subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Arch Phys Med Rehabil 2018; 99 (09) 1699-1709
50 Blumenthal-Barby JS, Krieger H. Cognitive biases and heuristics in medical decision making: a critical review using a systematic search strategy. Med Decis Making 2015; 35 (04) 539-557
51 Turgeon AF, Dorrance K, Archambault P. et al; Canadian Traumatic Brain Injury Research Consortium and the Canadian Critical Care Trials Group. Factors influencing decisions by critical care physicians to withdraw life-sustaining treatments in critically ill adult patients with severe traumatic brain injury. CMAJ 2019; 191 (24) E652-E663
52 Mertens M, King OC, van Putten M, Boenink M. Can we learn from hidden mistakes? Self-fulfilling prophecy and responsible neuroprognostic innovation. J Med Ethics 2021; 48 (11) 922-928
53 Wilson M, Staniforth A, Till R, das Nair R, Vesey P. The psychosocial outcomes of anoxic brain injury following cardiac arrest. Resuscitation 2014; 85 (06) 795-800
54 Bruno MA, Bernheim JL, Ledoux D, Pellas F, Demertzi A, Laureys S. A survey on self-assessed well-being in a cohort of chronic locked-in syndrome patients: happy majority, miserable minority. BMJ Open 2011; 1 (01) e000039
55 Albrecht GL, Devlieger PJ. The disability paradox: high quality of life against all odds. Soc Sci Med 1999; 48 (08) 977-988
56 Rousseau M-C, Baumstarck K, Alessandrini M, Blandin V, Billette de Villemeur T, Auquier P. Quality of life in patients with locked-in syndrome: evolution over a 6-year period. Orphanet J Rare Dis 2015; 10 (01) 88
57 Malenka DJ, Baron JA, Johansen S, Wahrenberger JW, Ross JM. The framing effect of relative and absolute risk. J Gen Intern Med 1993; 8 (10) 543-548
58 Gong J, Zhang Y, Yang Z, Huang Y, Feng J, Zhang W. The framing effect in medical decision-making: a review of the literature. Psychol Health Med 2013; 18 (06) 645-653
59 White DB, Ernecoff N, Buddadhumaruk P. et al. Prevalence of and factors related to discordance about prognosis between physicians and surrogate decision makers of critically ill patients. JAMA 2016; 315 (19) 2086-2094
60 Wartenberg KE, Hwang DY, Haeusler KG. et al. Gap analysis regarding prognostication in neurocritical care: a joint statement from the German Neurocritical Care Society and the Neurocritical Care Society. Neurocrit Care 2019; 31 (02) 231-244
61 Reynolds JM. The ableism of quality of life judgments in disorders of consciousness: who bears epistemic responsibility?. AJOB Neurosci 2016; 7 (01) 59-61
62 Binkley CE, Reynolds JM, Shuman A. From the eyeball test to the algorithm - quality of life, disability status, and clinical decision making in surgery. N Engl J Med 2022; 387 (14) 1325-1328
63 Jones K, Quinn T, Mazor KM, Muehlschlegel S. Prognostic uncertainty in critically ill patients with traumatic brain injury: a multicenter qualitative study. Neurocrit Care 2021; 35 (02) 311-321
64 Bailo L, Vergani L, Pravettoni G. Patient preferences as guidance for information framing in a medical shared decision-making approach: the bridge between nudging and patient preferences. Patient Prefer Adherence 2019; 13: 2225-2231
65 Muehlschlegel S, Goostrey K, Flahive J, Zhang Q, Pach JJ, Hwang DY. A pilot randomized clinical trial of a goals-of-care decision aid for surrogates of severe acute brain injury patients. Neurology 2022; 99 (14) e1446-e1455
66 Elwyn G, Frosch D, Thomson R. et al. Shared decision making: a model for clinical practice. J Gen Intern Med 2012; 27 (10) 1361-1367
67 Warren AM, Rainey EE, Weddle RJ, Bennett M, Roden-Foreman K, Foreman ML. The intensive care unit experience: psychological impact on family members of patients with and without traumatic brain injury. Rehabil Psychol 2016; 61 (02) 179-185
68 Gerritsen RT, Hartog CS, Curtis JR. New developments in the provision of family-centered care in the intensive care unit. Intensive Care Med 2017; 43 (04) 550-553
69 Lou W, Granstein JH, Wabl R, Singh A, Wahlster S, Creutzfeldt CJ. Taking a chance to recover: families look back on the decision to pursue tracheostomy after severe acute brain injury. Neurocrit Care 2022; 36 (02) 504-510
70 Kreitzer N, Bakas T, Kurowski B. et al. The experience of caregivers following a moderate to severe traumatic brain injury requiring ICU admission. J Head Trauma Rehabil 2020; 35 (03) E299-E309
71 Svenaeus F. Das unheimliche–towards a phenomenology of illness. Med Health Care Philos 2000; 3 (01) 3-16
72 Fulford KW. Praxis makes perfect: illness as a bridge between biological concepts of disease and social conceptions of health. Theor Med 1993; 14 (04) 305-320
73 Lindsey E. Health within illness: experiences of chronically ill/disabled people. J Adv Nurs 1996; 24 (03) 465-472
74 Nordeson A, Engström B, Norberg A. Self-reported quality of life for patients with progressive neurological diseases. Qual Life Res 1998; 7 (03) 257-266
75 Carel H, Can I. Be ill and happy?. Philosophia 2007; 35 (02) 95-110
76 Mitchell P. Adaptive preferences, adapted preferences. Mind 2018; 127 (508) 1003-1025
77 Klausen SH, Emiliussen J, Christiansen R, Hasandedic-Dapo L, Engelsen S. The many faces of hedonic adaptation. Philos Psychol 2022; 35 (02) 253-278
78 Neils-Strunjas J, Paul D, Clark AN. et al. Role of resilience in the rehabilitation of adults with acquired brain injury. Brain Inj 2017; 31 (02) 131-139
79 Pope TM, Bennett J, Carson SS. et al. Making medical treatment decisions for unrepresented patients in the ICU. An official American Thoracic Society/American Geriatrics Society Policy Statement. Am J Respir Crit Care Med 2020; 201 (10) 1182-1192
80 Weber E. Treatment decisions for unrepresented patients: American Thoracic Society/American Geriatrics Society policy statement lacks sufficient guidance. Am J Respir Crit Care Med 2020; 202 (10) 1483-1484
81 Steinberg A, Grayek E, Arnold RM. et al. Physicians' cognitive approach to prognostication after cardiac arrest. Resuscitation 2022; 173: 112-121
82 Zahuranec DB, Fagerlin A, Sánchez BN. et al. Variability in physician prognosis and recommendations after intracerebral hemorrhage. Neurology 2016; 86 (20) 1864-1871
83 Finley Caulfield A, Gabler L, Lansberg MG. et al. Outcome prediction in mechanically ventilated neurologic patients by junior neurointensivists. Neurology 2010; 74 (14) 1096-1101
84 Beauchamp TLCJ. Principles of biomedical ethics. Oxford: Oxford University Press; 1994
85 Giacino JT, Katz DI, Schiff ND. et al. Comprehensive systematic review update summary: disorders of consciousness: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Arch Phys Med Rehabil 2018; 99 (09) 1710-1719
86 Helbok R, Rass V, Beghi E. et al; Curing Coma Campaign and its Contributing Members. The Curing Coma Campaign International Survey on Coma Epidemiology, Evaluation, and Therapy (COME TOGETHER). Neurocrit Care 2022; 37 (01) 47-59
87 Young MJ, Bodien YG, Freeman HJ, Fecchio M, Edlow BL. Toward uniform insurer coverage for functional MRI following severe brain injury. J Head Trauma Rehabil 2023; 38 (04) 351-357
88 Mainali S, Aiyagari V, Alexander S. et al. Proceedings of the Second Curing Coma Campaign NIH Symposium: challenging the future of research for coma and disorders of consciousness. Neurocrit Care 2022; 37 (01) 326-350
89 Mainali S, Park S. Artificial intelligence and big data science in neurocritical care. Crit Care Clin 2023; 39 (01) 235-242
90 Fidali BC, Stevens RD, Claassen J. Novel approaches to prediction in severe brain injury. Curr Opin Neurol 2020; 33 (06) 669-675
91 Steyerberg EW, Mushkudiani N, Perel P. et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008; 5 (08) e165 , discussion e165
92 Perel P, Arango M, Clayton T. et al; MRC CRASH Trial Collaborators. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 2008; 336 (7641) 425-429
93 Roozenbeek B, Lingsma HF, Lecky FE. et al; International Mission on Prognosis Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) Study Group, Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, Trauma Audit and Research Network (TARN). Prediction of outcome after moderate and severe traumatic brain injury: external validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models. Crit Care Med 2012; 40 (05) 1609-1617
94 Lingsma H, Andriessen TM, Haitsema I. et al. Prognosis in moderate and severe traumatic brain injury: external validation of the IMPACT models and the role of extracranial injuries. J Trauma Acute Care Surg 2013; 74 (02) 639-646
95 Gautschi OP, Huser MC, Smoll NR. et al. Long-term neurological and neuropsychological outcome in patients with severe traumatic brain injury. Clin Neurol Neurosurg 2013; 115 (12) 2482-2488
96 de Cássia Almeida Vieira R, Silveira JCP, Paiva WS. et al. Prognostic models in severe traumatic brain injury: a systematic review and meta-analysis. Neurocrit Care 2022; 37 (03) 790-805
97 Bulgarelli L, Deliberato RO, Johnson AEW. Prediction on critically ill patients: the role of “big data”. J Crit Care 2020; 60: 64-68
98 Mainali S, Darsie ME, Smetana KS. Machine learning in action: stroke diagnosis and outcome prediction. Front Neurol 2021; 12: 734345
99 Ack SE, Loiseau SY, Sharma G. et al. Neurocritical care performance measures derived from electronic health record data are feasible and reveal site-specific variation: a CHoRUS pilot project. Neurocrit Care 2022; 37 (2, suppl 2): 276-290
100 Rebitzer JB, Rege M, Shepard C. Influence, information overload, and information technology in health care. Adv Health Econ Health Serv Res 2008; 19: 43-69
101 Claassen J, Doyle K, Matory A. et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med 2019; 380 (26) 2497-2505
102 Young MJ, Edlow BL. The quest for covert consciousness: bringing neuroethics to the bedside. Neurology 2021; 96 (19) 893-896
103 Ebrahimian S, Kalra MK, Agarwal S. et al. FDA-regulated AI algorithms: trends, strengths, and gaps of validation studies. Acad Radiol 2022; 29 (04) 559-566
104 Mukherjee P, Shen TC, Liu J, Mathai T, Shafaat O, Summers RM. Confounding factors need to be accounted for in assessing bias by machine learning algorithms. Nat Med 2022; 28 (06) 1159-1160
105 Durán JM, Jongsma KR. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J Medical Ethics 2021; 47 (05) 329-335
106 Ienca M, Ferretti A, Hurst S, Puhan M, Lovis C, Vayena E. Considerations for ethics review of big data health research: A scoping review. PLoS One 2018; 13 (10) e0204937
107 Ienca M, Ignatiadis K. Artificial intelligence in clinical neuroscience: methodological and ethical challenges. AJOB Neurosci 2020; 11 (02) 77-87
108 Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci U S A 2019; 116 (44) 22071-22080
109 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366 (6464) 447-453
110 Misiura MB, Howell JC, Wu J. et al. Race modifies default mode connectivity in Alzheimer's disease. Transl Neurodegener 2020; 9: 8
111 Li J, Bzdok D, Chen J. et al. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Sci Adv 2022; 8 (11) eabj1812
112 De-Arteaga M, Elmer J. Self-fulfilling prophecies and machine learning in resuscitation science. Resuscitation 2023; 183: 109622
113 Blumenthal-Barby J, An AI. Bill of rights: implications for health care AI and machine learning-a bioethics lens. Am J Bioeth 2022; 23 (01) 4-6
114 Adler-Milstein J, Aggarwal N, Ahmed M. et al. Meeting the moment: addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis. NAM Perspect 2022; 2022: 10.31478/202209c