Evidence map›Paper›PMID 39857626›Full record

ReviewBiomedicines2024

Harnessing the Power of AI to Improve Detection, Monitoring, and Public Health Interventions for Japanese Encephalitis.

Junhua Xiao, Evie Kendal, Faith A A Kwa

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Junhua XiaoSchool of Health Sciences, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.ORCID 0000-0002-4320-2855
Evie KendalSchool of Health Sciences, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.ORCID 0000-0002-8414-0427
Faith A A KwaSchool of Health Sciences, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.ORCID 0000-0002-9702-0563

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Japanese Encephalitis (JE) is the leading cause of viral encephalitis in regions with endemic Japanese Encephalitis Virus (JEV) infections. BACKGROUND/

objectivesThe aim of this review is to consider the potential role of artificial intelligence (AI) to improve detection, monitoring and public health interventions for JE. DISCUSSION: As climate change continues to impact mosquito population growth patterns, more regions will be affected by mosquito-borne diseases, including JE. Improving diagnosis and surveillance, while continuing preventive measures, such as widespread vaccination campaigns in endemic regions, will be essential to reduce morbidity and mortality associated with JEV.

conclusionsWith careful integration, AI mathematical and mechanistic models could be useful tools for combating the growing threat of JEV infections globally.

Indexed as

AIartificial intelligenceJapanese EncephalitisJapanese Encephalitis VirusJEJEV

Identifiers

PMID39857626
PMCPMC11763293

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.