ReviewWorld journal of critical care medicine2021
Predictive modeling in neurocritical care using causal artificial intelligence.
Review in World journal of critical care medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed.
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- AI and Neurology.Neurological research and practice · 2025Review
- Digital twins for health: a scoping review.NPJ digital medicine · 2024Article
- Model-driven engineering for digital twins: a graph model-based patient simulation application.Frontiers in physiology · 2024Article
- Gaining consensus on expert rule statements for acute respiratory failure digital twin patient model in intensive care unit using a Delphi method.Biomolecules & biomedicine · 2023Article
- Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.BMC neurology · 2023Article
- Development and usability testing of a patient digital twin for critical care education: a mixed methods study.Frontiers in medicine · 2023Article
- Regulatory oversight and ethical concerns surrounding software as medical device (SaMD) and digital twin technology in healthcare.Annals of translational medicine · 2022Article
- A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness.Annals of translational medicine · 2022Article
- The Digital Twin in Medicine: A Key to the Future of Healthcare?Frontiers in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.