Evidence map›Paper›PMID 39130982›Full record

ReviewCureus2024

Applications of Fog Computing in Healthcare.

Naveen Jeyaraman, Madhan Jeyaraman, Sankalp Yadav, Swaminathan Ramasubramanian, Sangeetha Balaji, Sathish Muthu, Chithra Lekha P, Bishnu P Patro

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

8 authors.

Naveen JeyaramanOrthopaedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND.
Madhan JeyaramanClinical Research, Virginia Tech India, Dr. MGR Educational and Research Institute, Chennai, IND.
Sankalp YadavMedicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND.
Swaminathan RamasubramanianOrthopaedics, Government Medical College, Omandurar Government Estate, Chennai, IND.
Sangeetha BalajiOrthopaedics, Government Medical College, Omandurar Government Estate, Chennai, IND.
Sathish MuthuOrthopaedics and Traumatology, Orthopaedic Research Group, Coimbatore, IND.
Chithra Lekha PClinical Research, Virginia Tech India, Dr. MGR Educational and Research Institute, Chennai, IND.
Bishnu P PatroOrthopaedics, All India Institute of Medical Sciences, Bhubaneswar, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fog computing is a decentralized computing infrastructure that processes data at or near its source, reducing latency and bandwidth usage. This technology is gaining traction in healthcare due to its potential to enhance real-time data processing and decision-making capabilities in critical medical scenarios. A systematic review of existing literature on fog computing in healthcare was conducted. The review included searches in major databases such as PubMed, IEEE Xplore, Scopus, and Google Scholar. The search terms used were "fog computing in healthcare," "real-time diagnostics and fog computing," "continuous patient monitoring fog computing," "predictive analytics fog computing," "interoperability in fog computing healthcare," "scalability issues fog computing healthcare," and "security challenges fog computing healthcare." Articles published between 2010 and 2023 were considered. Inclusion criteria encompassed peer-reviewed articles, conference papers, and review articles focusing on the applications of fog computing in healthcare. Exclusion criteria were articles not available in English, those not related to healthcare applications, and those lacking empirical data. Data extraction focused on the applications of fog computing in real-time diagnostics, continuous monitoring, predictive analytics, and the identified challenges of interoperability, scalability, and security. Fog computing significantly enhances diagnostic capabilities by facilitating real-time data analysis, crucial for urgent diagnostics such as stroke detection, by processing data closer to its source. It also improves monitoring during surgeries by enabling real-time processing of vital signs and physiological parameters, thereby enhancing patient safety. In chronic disease management, continuous data collection and analysis through wearable devices allow for proactive disease management and timely adjustments to treatment plans. Additionally, fog computing supports telemedicine by enabling real-time communication between remote specialists and patients, thereby improving access to specialist care in underserved regions. Fog computing offers transformative potential in healthcare, improving diagnostic precision, patient monitoring, and personalized treatment. Addressing the challenges of interoperability, scalability, and security will be crucial for fully realizing the benefits of fog computing in healthcare, leading to a more connected and efficient healthcare environment.

Indexed as

artificial intelligencedata securityfog computinghealthcare technologymedical diagnosticsreal-time data processing

Identifiers

PMID39130982
PMCPMC11315376

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.