The assessment of neurocritical patients may be complex; they are a vulnerable population, according to the Helsinki Declaration. Currently, the use of artificial intelligence (AI) in critical care settings may pose a dilemma for medico-legal reasons (lack of legal liability, privacy of data protection, difficulty in accessing medical records in judicial proceedings, no official medical guidelines on critical care promoting its use, and others). This paper considers the ethical-legal issues regarding its current medical use.
GutierrezG.Artificial intelligence in the intensive care unit. Critical Care2020; 24(1): 101.
2.
MoorJ.The Dartmouth College artificial intelligence conference: The next fifty years. AI Magazine2006; 27(4): 87–87.
3.
SendersJTZakiMMKarhadeAV, et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien)2018; 160(1): 29–38.
4.
SharmaRSalmanSGuQ, et al. Advancing neurocritical care with artificial intelligence and machine learning: The promise, practicalities, and pitfalls ahead. Neurol Clin2025; 43(1): 153–165.
5.
KimKAKimHHaEJ, et al. Artificial intelligence-enhanced neurocritical care for traumatic brain injury: Past, present and future. Journal of Korean Neurosurgical Society2024; 67(5): 493–509.
6.
KhaliliHRismaniMNematollahiMA, et al. Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Scientific Reports2023; 13(1): 960.
7.
HabliILawtonTPorterZ.Artificial intelligence in health care: accountability and safety. Bull World Health Organ2020; 98(4): 251–256.
8.
AlamiHLehouxPDenisJL, et al. Organizational readiness for artificial intelligence in health care: insights for decision-making and practice. J Health Organ Manag2020; 35(1): 106–114.
9.
World Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human participants. JAMA2025; 333(1): 71–74.
10.
CarneyNTottenAMO’ReillyC, et al. Guidelines for the management of severe traumatic brain injury, fourth edition. Neurosurgery2017; 80(1): 6–15.
11.
SaenzAD; Mass General Brigham AI Governance Committee, CentiA, et al. Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions. NPJ Digit Med2024; 7(1): 348.
12.
Abdul-RahmanTGhoshSLukmanL, et al. Inaccessibility and low maintenance of medical data archive in low-middle income countries: Mystery behind public health statistics and measures. J Infect Public Health2023; 16(10): 1556–1561.
13.
KhanBFatimaHQureshiA, et al. Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Biomed Mater Devices2023; 1: 731–738.
14.
MaasAIRMenonDKManleyGT, et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol2022; 21(11): 1004–1060. [Published correction appears in Lancet Neurol 2022; Dec 21(12): e10.]
15.
MaasAIRMenonDKAdelsonPD, et al. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol2017; 16(12): 987–1048.
16.
SniderSBDengHHammondFM, et al. Time to command-following and outcomes after traumatic brain injury. JAMA Netw Open2024; 7(12): e2449928.
17.
de MoraesFMRochaEBarrosFCD, et al. Waveform morphology as a surrogate for ICP monitoring: A comparison between an invasive and a noninvasive method. Neurocrit Care2022; 37: 219–227.
