Evidence map›Paper›PMID 37179277›Full record

ArticleAnnals of biomedical engineering2023

Artificial Intelligence in Intensive Care Medicine: Toward a ChatGPT/GPT-4 Way?

Yanqiu Lu, Haiyang Wu, Shaoyan Qi, Kunming Cheng

Abstract readLetter
In one paragraph

Article in Annals of biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  13. Large Language Model in Critical Care Medicine: Opportunities and Challenges.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2024
    Article
  14. 21st century critical care medicine: An overview.World journal of critical care medicine · 2024
    Review
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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

4 authors.

Yanqiu Lu *Department of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Haiyang Wu *Department of Graduate School, Tianjin Medical University, Tianjin, China.
Shaoyan QiDepartment of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. qishaoyan1970@163.com.
Kunming ChengDepartment of Intensive Care Unit, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. chengkm2013@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although intensive care medicine (ICM) is a relatively young discipline, it has rapidly developed into a full-fledged and highly specialized specialty covering several fields of medicine. The COVID-19 pandemic led to a surge in intensive care unit demand and also bring unprecedented development opportunities for this area. Multiple new technologies such as artificial intelligence (AI) and machine learning (ML) were gradually being applied in this field. In this study, through an online survey, we have summarized the potential uses of ChatGPT/GPT-4 in ICM range from knowledge augmentation, device management, clinical decision-making support, early warning systems, and establishment of intensive care unit (ICU) database.

Indexed as

Artificial IntelligenceCOVID-19Critical CareHumansIntensive Care UnitsPandemicsArtificial intelligenceBiomedical engineeringChatbotGPT-4New era

Identifiers

PMID37179277
PMCPMC10182840

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.