ArticleEuropean journal of anaesthesiology and intensive care2023
Entering the new digital era of intensive care medicine: an overview of interdisciplinary approaches to use artificial intelligence for patients' benefit.
Article in European journal of anaesthesiology and intensive care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan.BMC psychiatry · 2025Article
- Artificial Intelligence in Medical Care - Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Expectations of Anesthesiology and Intensive Care Professionals Toward Artificial Intelligence: Observational Study.JMIR formative research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The idea of implementing artificial intelligence in medicine is as old as artificial intelligence itself. So far, technical difficulties have prevented the integration of artificial intelligence in day-to-day healthcare. During the coronavirus disease 2019 (COVID-19) pandemic, a substantial amount of funding went into projects to research and implement artificial intelligence in healthcare. So far, artificial intelligence-based tools have had little impact in the fight against COVID-19. The reasons for the lack of success are complex. With advancing digitalisation, new data-based developed methods and research are finding their way into intensive care medicine. Data scientists and medical professionals, representing two different worlds, are slowly uniting. These two highly specialised fields do not yet speak a uniform language. Each field has its own interests and objectives. We took this idea as a starting point for this technical guide and aim to provide a deeper understanding of the terminology, applications, opportunities and risks of such applications for physicians. The most important terms in the field of machine learning are defined within a medical context to assure that the same language is spoken. The future of artificial intelligence applications will largely depend on the ability of artificial intelligence experts and physicians to cooperate in order to release the true power of artificial intelligence. Large research consortia, covering both technical and medical expertise, will grow because of growing demand in the future.
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.