Innovation, Technology, and Telemedicine in Critical Care.

Ahmed Shaikh, Dhruv Patel, Ankit Sakhuja

Abstract readReview

In one paragraph

Review in Critical care clinics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ahmed ShaikhInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.

Dhruv PatelInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.

Ankit SakhujaInstitute for Critical Care Medicine, Windreich Department of Artificial Intelligence and Human Health, Charles Bronfman Institute for Personalized Medicine, The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: ankit.sakhuja@mssm.edu.

Funding

Using Novel Machine Learning Methods to Personalize Strategies for Prevention of Persistent AKI after Cardiac SurgeryK08DK131286 · NIDDK · WEST VIRGINIA UNIVERSITY · PI Ankit Sakhuja · 2022 to 2026

$774k

NIDDK NIH HHS K08 DK131286

6 · The paper itself

Abstract

Critical care medicine is undergoing a major transformation driven by rapid technological innovation, digital integration, and telemedicine. This article examines how modern intensive care units (ICUs) are evolving into data-rich, precision environments equipped with advanced monitoring, organ support, and predictive analytics. Innovations such as artificial intelligence-enhanced decision support, portable ultrasound, and tele-ICU platforms are expanding diagnostic accuracy, remote access, and timely interventions. By tracing historical milestones to emerging technologies like smart ICUs and wearable biosensors, this article highlights the shift toward anticipatory, personalized care. It underscores the importance of evidence-based adoption to ensure these tools improve outcomes.

Indexed as

Critical CareIntensive Care UnitsInventionsTelemedicineArtificial IntelligenceDigital HealthHumansArtificial intelligenceCritical careOrgan support systemsSmart ICUsTele-ICU

Identifiers

PMID41825983

DOI10.1016/j.ccc.2025.11.003

PMCPMC13162213

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.