Evidence map›Paper›PMID 40752950›Full record

ReviewAnesthesiology clinics2025

Current Perspectives on the Artificial Intelligence in Critical Care Medicine.

Jongmin Lee, Joo Heung Yoon

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Anesthesiology clinics, 2025. 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. 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

2 authors.

Jongmin LeeDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho District, Seoul, South Korea.
Joo Heung YoonDivision of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Pittsburgh, Montefiore NW628, 3459 Fifth Avenue, Pittsburgh, PA 15213, USA. Electronic address: yoonjh@upmc.edu.

Funding

Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill PatientsK23GM138984 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI YOON, JOO HEUNG · 2020 to 2024
$940k
Designing a Comprehensive Machine Learning-driven Prescriptive Clinical Decision Support System for Shock and Associated Critical Care ConditionsR35GM159939 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Joo Heung Yoon · 2025 to 2026
$867k
NIGMS NIH HHS K23 GM138984NIGMS NIH HHS R35 GM159939
6 · The paper itself

Abstract

The article explores the transformative impact of artificial intelligence (AI) in critical care medicine. AI models offer superior accuracy in mortality risk assessment and personalized treatment recommendations, enhancing patient outcomes in acute and complex settings. However, various challenges exist, such as data quality, model interpretability, integrating models to clinical workflow, and ethical and legal concerns need to be addressed for successful integration into clinical practice. The article emphasizes the demand for ongoing research on data quality and standardization, building model transparency, developing robust validation methods, and ethical oversight to fully leverage AI's potential in improving critical care.

Indexed as

Artificial IntelligenceCritical CareHumansArtificial intelligenceCritical care medicineDeep learningFoundation modelLarge language model

Identifiers

PMID40752950
PMCPMC12817365

What OpenQuestion holds

Textmetadata
LicenceTDM
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.