Michard F. Hemodynamic monitoring in the era of digital health. Ann Intensive Care. 2016;6:15.

Article  PubMed  PubMed Central  Google Scholar 

Drews FA, Westenskow DR. The right picture is worth a thousand numbers: data displays in anesthesia. Hum Factors. 2006;48:59–71.

Article  PubMed  Google Scholar 

Görges M, Staggers N. Evaluations of physiological monitoring displays: a systematic review. J Clin Monit Comput. 2008;22:45–66.

Article  PubMed  Google Scholar 

Ford S, Birmingham E, King A, Lim J, Ansermino JM. At-a-glance monitoring: covert observations of anesthesiologists in the operating room. Anesth Analg. 2010;111:653–8.

Article  PubMed  Google Scholar 

Michard F, Pinsky MR, Vincent JL. Intensive care medicine in 2050: NEWS for hemodynamic monitoring. Intensive Care Med. 2017;43:440–2.

Article  PubMed  Google Scholar 

Kouz K, Scheeren TWL, de Backer D, Saugel B. Pulse wave analysis to estimate cardiac output. Anesthesiology. 2021;134:119–26.

Article  PubMed  Google Scholar 

Kouz K, Thiele R, Michard F, Saugel B. Haemodynamic monitoring during non-cardiac surgery: past, present, and future. J Clin Monit Comput. 2024;38:565–80.

Article  PubMed  PubMed Central  Google Scholar 

Miller GA. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev. 1994;101:343–52.

Article  CAS  PubMed  Google Scholar 

Kouz K, Brockmann L, Timmermann LM, et al. Endotypes of intraoperative hypotension during major abdominal surgery: a retrospective machine learning analysis of an observational cohort study. Br J Anaesth. 2023;130:253–61.

Article  PubMed  Google Scholar 

Michard F, Foss NB, Bignami E. Hemodynamic profiling: When AI tells us what we already know. Br J Anaesth 2025; In press.

Gurushanthaiah K, Weinger MB, Englund CE. Visual display format affects the ability of anesthesiologists to detect acute physiologic changes. A laboratory study employing a clinical display simulator. Anesthesiology. 1995;83:1184–93.

Article  CAS  PubMed  Google Scholar 

Blike GT, Surgenor SD, Whalen K. A graphical object display improves anesthesiologists’ performance on a simulated diagnostic task. J Clin Monit Comput. 1999;15:37–44.

Article  CAS  PubMed  Google Scholar 

Vallée F, Fourcade O, Marty P, Sanchez P, Samii K, Genestal M. The hemodynamic “target”: a visual tool of goal-directed therapy for septic patients. Clinics (Sao Paulo). 2007;62:447–54.

Article  PubMed  Google Scholar 

Michard F. Decision support for hemodynamic management: from graphical displays to closed loop systems. Anesth Analg. 2013;117:876–82.

Article  PubMed  Google Scholar 

Agutter J, Drews F, Syroid N, Westneskow D, Albert R, Strayer D, Bermudez J, Weinger MB. Evaluation of graphic cardio-vascular display in a high-fidelity simulator. Anesth Analg. 2003;97:1403–13.

Article  PubMed  Google Scholar 

Albert RW, Agutter JA, Syroid ND, Johnson KB, Loeb RG, Westenskow DR. A simulation-based evaluation of a graphic cardiovascular display. Anesth Analg. 2007;105:1303–11.

Article  PubMed  Google Scholar 

Gasciauskaite G, Lunkiewicz J, Roche TR, et al. Human-centered visualisation technologies for patient monitoring are the future: a narrative review. Crit Care. 2023;27:254.

Article  PubMed  PubMed Central  Google Scholar 

Pfarr J, Ganter MT, Spahn DR, et al. Effects of a standardized distraction on caregivers’ perceptive performance with avatar-based and conventional patient monitoring: a multicenter comparative study. J Clin Monit Comput. 2020;34:1369–78.

Article  PubMed  Google Scholar 

Bergauer L, Braun J, Roche TR, et al. Avatar-based patient monitoring improves information transfer, diagnostic confidence and reduces perceived workload in intensive care units: computer-based, multicentre comparison study. Sci Rep. 2023;13:5908.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Orde S, Slama M, Hilton A, et al. Pearls and pitfalls in comprehensive critical care echocardiography. Crit Care. 2017;21:279.

Article  PubMed  PubMed Central  Google Scholar 

Vieillard-Baron A, Millington SJ, Sanfilippo F, et al. A decade of progress in critical care echocardiography: a narrative review. Intensive Care Med. 2019;45:770–88.

Article  PubMed  Google Scholar 

Mayo PH, Chew M, Douflé M, et al. Machines that save lives in the intensive care unit: the ultrasonography machine. Intensive Care Med. 2022;48:1429–38.

Article  PubMed  PubMed Central  Google Scholar 

Cecconi M, De Backer D, Antonelli M, et al. Consensus on circulatory shock and hemodynamic monitoring. Task force of the european society of intensive care medicine. Intensive Care Med. 2014;40:1795–815.

Article  PubMed  PubMed Central  Google Scholar 

Prinz C, Voigt JU, Piper C. Diagnostic accuracy of a pocket-sized ultrasound scanner in routine patients referred for echocardiography. Cardiovasc Ultrasound. 2018;16:1–7.

Google Scholar 

Le MPT, Voigt L, Nathanson R, et al. Comparison of four handheld point of care ultrasound devices by expert users. Ultrasound J. 2022;14:27.

Article  PubMed  PubMed Central  Google Scholar 

Bacariza J, Gonzalez FA, Varudo R, et al. Smartphone-based automatic assessment of left ventricular ejection fraction with a silicon chip ultrasound probe: a prospective comparison study in critically ill patients. Br J Anaesth. 2023;130:e485–7.

Article  CAS  PubMed  Google Scholar 

Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73:1317–35.

Article  PubMed  PubMed Central  Google Scholar 

Nabi W, Bansal A, Xu B. Applications of artificial intelligence and machine learning approaches in echocardiography. Echocardiography. 2021;38:982–92.

Article  PubMed  Google Scholar 

Narang A, Bae R, Hong H, et al. Using a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol. 2021;6:624–32.

Article  PubMed  Google Scholar 

Choi KJ, Jang JY, Kim HS, et al. Improvement in the accuracy of ultrasound-based measurement of left ventricular ejection fraction by a deep learning algorithm. Eur Heart J-Cardiovas Imaging. 2020;21:24–32.

Google Scholar 

Schneider M, Bartko P, Geller W, et al. A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF. Int J Cardiovasc Imaging. 2021;37:577–86.

Article  PubMed  Google Scholar 

Asch FM, Mor-Avi V, Rubenson D, et al. Deep-learning based automated echocardiographic quantification of left ventricular ejection fraction: a point-of-care solution. Circ Cardiovasc Imaging. 2021;14: e012293.

Article  PubMed  Google Scholar 

Varudo R, Gonzalez FA, Leote J, et al. Machine learning for the real time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography. Crit Care. 2022;26:386.

Article  PubMed  PubMed Central  Google Scholar 

Asch FM, Poilvert N, Abraham T, et al. Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circ Cardiovasc Imaging. 2019;12: e009303.

Article  PubMed  PubMed Central  Google Scholar 

Mercado P, Maizel J, Beyls C, et al. Transthoracic echocardiography: an accurate and precise method for estimating cardiac output in the critically ill patient. Crit Care. 2017;21:136.

Article  PubMed