ReviewWorld journal of critical care medicine2020
Artificial intelligence and computer simulation models in critical illness.
Review in World journal of critical care medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- International Delphi consensus on acute kidney injury: Foundations for AI-driven digital twin development in critical care nephrology.PloS one · 2026Article
- Development and external validation of an artificial intelligence model for predicting mortality and prolonged ICU stay in postoperative critically ill patients: a retrospective study.World journal of emergency surgery : WJES · 2025Article
- The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool.AI and ethics · 2025Article
- 21st century critical care medicine: An overview.World journal of critical care medicine · 2024Review
- 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
- Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- 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
- The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field.NPJ digital medicine · 2022Review
- A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness.Annals of translational medicine · 2022Article
- Editorial: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume II.Frontiers in medicine · 2022Article
- Predictive modeling in neurocritical care using causal artificial intelligence.World journal of critical care medicine · 2021Review
- Utility of AI models in critical care: union of man and the machine.Critical care (London, England) · 2021Article
- Editorial: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume I.Frontiers in medicine · 2021Article
- Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis.Critical care explorations · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven "associative" AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.
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.