Evidence map›Paper›PMID 39764208›Full record

ReviewFrontiers in digital health2024

Artificial intelligence in respiratory care.

Manjush Karthika, Jithin K Sreedharan, Madhuragauri Shevade, Chris Sara Mathew, Santosh Ray

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
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

5 authors.

Manjush KarthikaFaculty of Medical and Health Sciences, Liwa College, Abu Dhabi, United Arab Emirates.
Jithin K SreedharanDepartment of Respiratory Therapy, University of Doha for Science and Technology, Doha, Qatar.
Madhuragauri ShevadeDepartment of Training Programs, Chest Research & Training Pvt Ltd., Pune, India.
Chris Sara MathewFaculty of Medical and Health Sciences, Liwa College, Abu Dhabi, United Arab Emirates.
Santosh RayFaculty of Engineering and Computing, Liwa College, Abu Dhabi, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The evolution of artificial intelligence (AI) has revolutionised numerous aspects of our daily lives, with profound implications across various sectors, including healthcare. Although the concept of AI in healthcare was introduced in the early 1970s, the integration of this technology in healthcare is still in the evolution phase. Despite barriers, the current decade is witnessing an increased utility of AI into diverse specialities of the medical field to enhance precision medicine, predict diagnosis, therapeutic results, and prognosis; this includes respiratory medicine, critical care, and in their allied specialties. AI algorithms are widely studied in areas like mechanical ventilation, sleep medicine, lung ultrasound, and pulmonary function diagnostics and the results are found to be promising. The quality of patient care and safety can be greatly enhanced if respiratory care professionals fully understand the concept and importance of AI, as they are already incorporating various aspects of this technology into their clinical practice. Awareness of AI in the clinical field is essential during this phase; hence, it is desirable to establish widely accepted standards presented in a clear and accessible language. This article aims to describe the existing and prospective role of AI in the field of respiratory care and allied areas.

Indexed as

artificial intelligencedeep learningmachine learningmechanical ventilationrespiratory care

Identifiers

PMID39764208
PMCPMC11700984

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.