ReviewHealth science reports2026
A Review of the Role of Artificial Intelligence in Patients on Extracorporeal Membrane Oxygenation: A Scoping Review Study.
Review in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: ECMO is used to deliver cardiopulmonary support in extreme failure where standard therapies are unfruitful. Although ECMO is associated with high costs and significant risks, advances in technology and clinical management have improved its safety and patient outcomes. AI/ML may provide decision support through data analysis to alert clinicians to potential safety issues, detect complications, and help inform pump/ventilator settings. The primary objective of this scoping review is to examine the applications of AI and machine learning in patients receiving various ECMO modalities, specifically veno-arterial (VA), veno-venous (VV), and extracorporeal cardiopulmonary resuscitation (ECPR). Methods: We carried out a scoping review based on Arksey and O'Malley, encompassing 20 inaugural AI/ML ECMO studies (no review/theoretical/non-English). The databases were searched till December 2025. Expressed data covered study characteristics, AI methods, inputs, objectives, performance, and limitations. Algorithm applications were categorized based on patient characteristics and clinical use (selection, timing, monitoring, weaning, and readmission). The findings were expert-validated to identify trends and gaps. Results: Across studies ( Conclusion: AI/ML are promising for predicting ECMO needs and outcomes and may support clinical decision-making, but retrospective data and limited prospective validation temper conclusions. Prospective and real‑world validation studies are required to demonstrate clinical outcome benefits and support the integration of AI into ECMO practice and guidelines.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.