Evidence map›Paper›PMID 39328566›Full record

ReviewHeliyon2024

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects.

Siti Nur Ashakirin Binti Mohd Nashruddin, Faridah Hani Mohamed Salleh, Rozan Mohamad Yunus, Halimah Badioze Zaman

Abstract readReview
In one paragraph

Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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  14. A simple linear sweep voltammetric method for the detection of glucose using an Ag-Au/MWCNT nanocomposite-modified glassy carbon electrode.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026
    Article
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  18. Advances in biosensors for bacterial detection and identification.World journal of microbiology & biotechnology · 2025
    Review
  19. TiOMicromachines · 2025
    Review
  20. 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

4 authors.

Siti Nur Ashakirin Binti Mohd NashruddinInstitute of Informatics and Computing in Energy (IICE), Department of Computing, College of Computing & Informatics, Universiti Tenaga Nasional, 43000, Kajang, Selangor Darul Ehsan, Malaysia.
Faridah Hani Mohamed SallehInstitute of Informatics and Computing in Energy (IICE), Department of Computing, College of Computing & Informatics, Universiti Tenaga Nasional, 43000, Kajang, Selangor Darul Ehsan, Malaysia.
Rozan Mohamad YunusFuel Cell Institute, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Halimah Badioze ZamanInstitute of Informatics and Computing in Energy (IICE), Department of Computing, College of Computing & Informatics, Universiti Tenaga Nasional, 43000, Kajang, Selangor Darul Ehsan, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

Indexed as

Artificial neural networksElectrochemicalEnergy savingGenetic algorithmMachine learning

Identifiers

PMID39328566
PMCPMC11425101

What Socratic 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 Socratic graph.