Evidence map›Paper›PMID 38066215›Full record

ReviewDie Anaesthesiologie2024

[Big data and artificial intelligence in anesthesia : Reality or fiction?]

J Sander, P Simon, C Hinske

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

Review in Die Anaesthesiologie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. [Artificial intelligence in intensive care medicine].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

J SanderInstitut für Digitale Medizin (IDM), Universitätsklinikum Augsburg, Gutenbergstr. 7, 86356, Neusäß, Deutschland. correspondence.sander@gmail.com.
P SimonKlinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Augsburg, Augsburg, Deutschland.
C HinskeInstitut für Digitale Medizin (IDM), Universitätsklinikum Augsburg, Gutenbergstr. 7, 86356, Neusäß, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Big data and artificial intelligence are buzzwords that everyone is talking about and yet always provide a touch of science fiction to the scenery. What is the status of these topics in anesthesia? Are the first robots already rolling through the corridors while doctors are getting bored as all the work has been done? Spoiler alert! We are still far away from achieving this. Initially, paper charts and analogue notes stand in the way of comprehensive digitization. Source systems need to be merged and data standardized, harmonized and validated. Therefore, the friendly android that is rolling towards us, waving and holding a freshly brewed cup of coffee in our thoughts will have to wait; however, a glimpse of the future is already evident in some clinics and the first promising developments are already showing what could be the standard tomorrow. Learning algorithms calculate the length of stay individually for each patient in the intensive care unit (ICU), reducing negative consequences such as readmission and mortality. The field of ultrasound technology for regional anesthesia and closed-loop anesthesia systems is also demonstrating the benefits of artificial intelligence (AI)-assisted technologies in practice. The efforts are diverse and ambitious but they repeatedly collide with privacy challenges and significant capital expenditure, which weigh heavily on an already financially strained healthcare system; however, anyone who listens carefully to the medical staff knows that robots are not what they would expect and the buzzwords big data and artificial intelligence might be less science fiction than initially assumed.

Indexed as

Anesthesia, ConductionPhysiciansAlgorithmsArtificial IntelligenceBig DataHumansClinical decision supportData protectionDigitizationLearning algorithmsPersonalized medical care

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.