metricas
Revista Española de Anestesiología y Reanimación (English Edition) Evolution of medical practice: From eminence to artificial intelligence
Journal Information
Cite
Cite
Share
Download PDF
More article options
Editorial article

Evolution of medical practice: From eminence to artificial intelligence

Evolución de la práctica médica: de la eminencia a la inteligencia artificial
Visits
1137
H.S. Manzolilloa, Á. Espinosab,c,
Corresponding author
drangelespinosa@gmail.com

Corresponding author.
, S. Benitezd, J.R. Melchorc,e,f
a Department of Technological Innovation, Instituto Cardiológico de Corrientes, Juana Francisca Cabral, Argentina
b Department of Anesthesia, King Salman Specialist Hospital, City of Hail, Kingdom of Saudi Arabia
c Fluid Therapy and Hemodynamic Monitoring Group of the Spanish Society of Anesthesiology and Critical Care, Madrid, Spain
d Department of Cardiovascular Imaging and Digital Health, Private Hospital of Rosario. City of Rosario, Argentina
e Department of Anesthesia, Infanta Leonor University Hospital, Madrid, Spain
f Universidad Complutense de Madrid, Spain
This item has received
Article information
Full Text
Bibliography
Download PDF
Statistics

Article

These are the options to access the full texts of the publication Revista Española de Anestesiología y Reanimación (English Edition)
Subscriber
Subscriber

If you already have your login data, please click here .

If you have forgotten your password you can you can recover it by clicking here and selecting the option “I have forgotten my password”
Purchase
Purchase article

Purchasing article the PDF version will be downloaded

Purchase now
Contact
Phone for subscriptions and reporting of errors
From Monday to Friday from 9 a.m. to 6 p.m. (GMT + 1) except for the months of July and August which will be from 9 a.m. to 3 p.m.
Calls from Spain
932 415 960
Calls from outside Spain
+34 932 415 960
E-mail
References
[1]
O. Ljungqvist, M. Scott, K.C. Fearon.
Enhanced recovery after surgery: a review.
JAMA Surg., 152 (2017), pp. 292-298
[2]
K.M. Sauro, C. Smith, S. Ibadin, et al.
Enhanced recovery after surgery guidelines and hospital length of stay, readmission, complications, and mortality: a meta-analysis of randomized clinical trials.
JAMA Netw Open, 7 (2024),
[3]
J. Ripollés-Melchor, A. Abad-Motos, M. Cecconi, et al.
Association between use of enhanced recovery after surgery protocols and postoperative complications in colorectal surgery in Europe: The EuroPOWER international observational study.
J Clin Anesth, 80 (2022),
[4]
J. Ripollés-Melchor, J.M. Ramírez-Rodríguez, R. Casans-Francés, POWER Study Investigators Group for the Spanish Perioperative Audit and Research Network (REDGERM), et al.
Association between use of enhanced recovery after surgery protocol and postoperative complications in colorectal surgery: the Postoperative Outcomes Within Enhanced Recovery After Surgery Protocol (POWER) study.
JAMA Surg, 154 (2019), pp. 725-736
[5]
A.K. Khanna, T.J. Gan.
Perioperative precision medicine and bedside decision making: still a case of great expectations?.
Anesth Analg, 134 (2022), pp. 896-899
[6]
D.N. Lobo, G.P. Joshi, H. Kehlet.
Challenges in Enhanced Recovery After Surgery (ERAS) research.
Br J Anaesth., 133 (2024), pp. 717-721
[7]
H.K. Yoon, H.L. Yang, C.W. Jung, H.C. Lee.
Artificial intelligence in perioperative medicine: a narrative review.
Korean J Anesthesiol., 75 (2022), pp. 202-215
[8]
C.M. Zhou, H. Li, Q. Xue, J.J. Yang, Y. Zhu.
Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery.
Heliyon., 10 (2024),
[9]
H. Kehlet.
Multimodal approach to control postoperative pathophysiology and rehabilitation.
Br J Anaesth., 78 (1997), pp. 606-617
[10]
R.A. Armstrong, A. Fayaz, G.L.P. Manning, et al.
Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset.
Anaesthesia., 78 (2023), pp. 840-852
[11]
C. Michelsen, C.C. Jørgensen, M. Heltberg, et al.
Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty—a comparative study.
BMC Anesthesiol., 23 (2023), pp. 391
[12]
H.A. Lim, M. Kim, N.J. Kim, J. Huh, J.O. Jeong, W. Hwang, et al.
The performance of continuous glucose monitoring during the intraoperative period: a scoping review.
J Clin Med., 13 (2024),
[13]
J. Ripollés-Melchor, A. Ruiz-Escobar, P. Fernández-Valdes-Bango, J.V. Lorente, I. Jiménez-López, A. Abad-Gurumeta, et al.
Hypotension prediction index: from reactive to predictive hemodynamic management, the key to maintaining hemodynamic stability.
Front Anesthesiol., 2 (2023),
[14]
S. Coeckelenbergh, M. Entzeroth, P. Van der Linden, M. Flick, M. Soucy-Proulx, B. Alexander, et al.
Assisted fluid management and sublingual microvascular flow during high-risk abdominal surgery: a randomized controlled trial.
[15]
R. Sajdeya, S. Narouze.
Harnessing artificial intelligence for predicting and managing postoperative pain: a narrative literature review.
Curr Opin Anaesthesiol., 37 (2024), pp. 604-615
[16]
A. Bishara, C. Chiu, E.L. Whitlock, et al.
Postoperative delirium prediction using machine learning models and preoperative electronic health record data.
BMC Anesthesiol., 22 (2022), pp. 8
[17]
Z. Zain, M.K.I.K. Almadhoun, L. Alsadoun, S.F.H. Bokhari.
Leveraging artificial intelligence and machine learning to optimize enhanced recovery after surgery (ERAS) protocols.
Cureus., 16 (2024),
[18]
J.T. Wright Jr, J.D. Williamson, SPRINT Research Group, et al.
A randomized trial of intensive versus standard blood-pressure control.
N Engl J Med., 373 (2015), pp. 2103-2116
[19]
K.W. Johnson, B.S. Glicksberg, R.A. Hodos, K. Shameer, J.T. Dudley.
Causal inference on electronic health records to assess blood pressure treatment targets: an application of the parametric g formula.
Pac Symp Biocomput, 23 (2018), pp. 180-191
[20]
G.S. Ginsburg, R.W. Picard, S.H. Friend.
Key issues as wearable digital health technologies enter clinical care.
N Engl J Med., 390 (2024), pp. 1118-1127
[21]
M. Reiser, U. Attenberger.
Künstliche Intelligenz in der Radiologie: Vom Gartner Hype Cycle zu Amara’s Law [Artificial intelligence in radiology: From the Gartner Hype Cycle to Amara’s Law].
Radiologie (Heidelb)., 64 (2024), pp. 749-751
[22]
J. Ripollés-Melchor, J.L. Tomé-Roca, A. Zorrilla-Vaca, et al.
Hemodynamic management guided by the Hypotension Prediction Index in abdominal surgery: a multicenter randomized clinical trial.
Anesthesiology, 142 (2025), pp. 639-654
[23]
J.W. Ayers, A. Poliak, M. Dredze, E.C. Leas, Z. Zhu, J.B. Kelley, et al.
Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum.
JAMA Intern Med, 183 (2023), pp. 589-596
asdasdasd
Article options
Tools