Evidence map›Paper›PMID 39290718›Full record

ArticleFrontiers in artificial intelligence2024

Artificial intelligence in respiratory care: knowledge, perceptions, and practices-a cross-sectional study.

Jithin K Sreedharan, Asma Alharbi, Amal Alsomali, Gokul Krishna Gopalakrishnan, Abdullah Almojaibel, Rawan Alajmi, Ibrahim Albalawi, Musallam Alnasser, Meshal Alenezi, Abdullah Alqahtani and 3 more

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Article in Frontiers in artificial intelligence, 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. 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

13 authors.

Jithin K SreedharanDepartment of Respiratory Therapy, College of Health Sciences, University of Doha for Science and Technology, Doha, Qatar.
Asma AlharbiDepartment of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Amal AlsomaliDepartment of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Gokul Krishna GopalakrishnanDepartment of Respiratory Care, Batterjee Medical College, Jeddah, Saudi Arabia.
Abdullah AlmojaibelDepartment of Respiratory Care, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Rawan AlajmiDepartment of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Ibrahim AlbalawiAdvanced Center for Clinical Simulation, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Musallam AlnasserDepartment of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Meshal AleneziDepartment of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Abdullah AlqahtaniDepartment of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Mohammed AlahmariDammam Health Network, Eastern Health Cluster, Dammam, Saudi Arabia.
Eidan AlzahraniDepartment of Physical Therapy, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.
Manjush KarthikaDepartment of Health and Medical Sciences, Liwa College, Abu Dhabi, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is reforming healthcare, particularly in respiratory medicine and critical care, by utilizing big and synthetic data to improve diagnostic accuracy and therapeutic benefits. This survey aimed to evaluate the knowledge, perceptions, and practices of respiratory therapists (RTs) regarding AI to effectively incorporate these technologies into the clinical practice. Methods: The study approved by the institutional review board, aimed at the RTs working in the Kingdom of Saudi Arabia. The validated questionnaire collected reflective insights from 448 RTs in Saudi Arabia. Descriptive statistics, thematic analysis, Fisher's exact test, and chi-square test were used to evaluate the significance of the data. Results: The survey revealed a nearly equal distribution of genders (51% female, 49% male). Most respondents were in the 20-25 age group (54%), held bachelor's degrees (69%), and had 0-5 years of experience (73%). While 28% had some knowledge of AI, only 8.5% had practical experience. Significant gender disparities in AI knowledge were noted ( Conclusion: In conclusion, this study highlights differences in the levels of knowledge and perceptions regarding AI among respiratory care professionals, underlining its recognized significance and futuristic awareness in the field. Tailored education and strategic planning are crucial for enhancing the quality of respiratory care, with the integration of AI. Addressing these gaps is essential for utilizing the full potential of AI in advancing respiratory care practices.

Indexed as

AIartificial intelligencechallengesintegrationintegration artificial intelligenceprofessionalsrespiratory carerespiratory therapy

Identifiers

PMID39290718
PMCPMC11405306

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Registered trials

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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.