Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximise decision speed – even if the AI is less accurate than human doctors.
GyawaliBRamakrishnaKDhamoonA. Sepsis: The evolution in definition, pathophysiology, and management. Open Med2019; 7: 1–13.
12.
KomorowskiMCeliLBadawiO, et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med2018; 24: 1716–1720.
13.
VincentJL, et al. Assessment of the worldwide burden of critical illness: the intensive care over nations (ICON) audit. The Lanc Respir Med. 2014; 2: 380–386.
14.
ZhaoXShenWWangG. Early prediction of sepsis based on machine learning algorithm. Comp Int Neu. 2021; 6522633.
15.
PasaFGolkovVPfeifferF, et al.Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci Rep.2019; 9: 6268.
16.
JoTNhoKSaykinA. Deep learning in Alzheimer's disease: Diagnostic classification and prognostic prediction using neuroimaging data. Front Agi Neu.2019; 11: 220–220.
17.
IslamM, et al.. Prediction of sepsis patients using machine learning approach: A meta-analysis. Comp Met Prog Bio.2019; 170: 1–9.
18.
FleurenL, et al.Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Int Care Med.2020; 46: 383–400.
Sendak, et al.Real-world integration of a sepsis deep learning technology into routine clinical care: Implementation study. JMIR Med Inform. 2020; 8: e15182.
KemptHNagelS. Responsibility, second opinions and peerdisagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts. Jour Med Eth. 2022; 48: 222–229.
23.
LopansriB, et al.Physician agreement on the diagnosis of sepsis in the intensive care unit: estimation of concordance and analysis of underlying factors in a multicenter cohort. Jour Int Care. 2019; 7: 1–17.
24.
ZerilliJKnottAMaclaurinJ, et al.. Transparency in algorithmic and human decision-making: Is there a double standard?. Phil Tech.2019; 32: 661–683.
25.
GüntherMKasirzadehA. Algorithmic and human decision making: for a double standard of transparency. AI & Soc.2021; 37: 375–381.
26.
PrescottH, et al.Late mortality after sepsis: propensity matched cohort study. Br Med J.2016; 353: i2375.
