Evidence map›Paper›PMID 36911247›Full record

ArticleComputational intelligence and neuroscience2022

Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues.

Anil Audumbar Pise, Khalid K Almuzaini, Tariq Ahamed Ahanger, Ahmed Farouk, Kumud Pant, Piyush Kumar Pareek, Stephen Jeswinde Nuagah

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. 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

7 authors.

Anil Audumbar PiseFinalMile Consultants Private Limited, Johannesburg, South Africa.ORCID https://orcid.org/0000-0002-2402-087X
Khalid K AlmuzainiNational Center for Cybersecurity Technologies, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0003-4458-3730
Tariq Ahamed AhangerCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.ORCID https://orcid.org/0000-0002-4525-0738
Ahmed FaroukDepartment of Computer Science, Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt.
Kumud PantDepartment of Biotechnology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
Piyush Kumar PareekDepartment of Computer Science & Engineering & Head of IPR Cell, Nitte Meenakshi Institute of Technology, Bengaluru, India.ORCID https://orcid.org/0000-0003-2287-0122
Stephen Jeswinde NuagahDepartment of Electrical Engineering, Tamale Technical University, Tamale, Ghana.ORCID https://orcid.org/0000-0001-5687-9649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A significant study has been undertaken in the areas of health care and administration of cutting-edge artificial intelligence (AI) technologies throughout the previous decade. Healthcare professionals studied smart gadgets and other medical technologies, along with the AI-based Internet of Things (IoT) (AIoT). Connecting the two regions makes sense in terms of improving care for rural and isolated resident individuals. The healthcare industry has made tremendous strides in efficiency, affordability, and usefulness as a result of new research options and major cost reductions. This includes instructions (AIoT-based) medical advancements can be both beneficial and detrimental. While the IoT concept undoubtedly offers a number of benefits, it also poses fundamental security and privacy concerns regarding medical data. However, resource-constrained AIoT devices are vulnerable to a number of assaults, which can significantly impair their performance. Cryptographic algorithms used in the past are inadequate for safeguarding IoT-enabled networks, presenting substantial security risks. The AIoT is made up of three layers: perception, network, and application, all of which are vulnerable to security threats. These threats can be aggressive or passive in nature, and they can originate both within and outside the network. Numerous IoT security issues, including replay, sniffing, and eavesdropping, have the ability to obstruct network communication. The AIoT-H application is likely to be explored in this research article due to its potential to aid with existing and different technologies, as well as bring useful solutions to healthcare security challenges. Additionally, every day, several potential problems and inconsistencies with the AIoT-H technique have been discovered.

Indexed as

Artificial IntelligenceComputer SecurityAlgorithmsDelivery of Health CareHumansPrivacy

Identifiers

PMID36911247
PMCPMC9998155

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.