Evidence map›Paper›PMID 40900374›Full record

ReviewVisual computing for industry, biomedicine, and art2025

Lightweight and mobile artificial intelligence and immersive technologies in aviation.

Graham Wild, Aziida Nanyonga, Anam Iqbal, Shehar Bano, Alexander Somerville, Luke Pollock

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Visual computing for industry, biomedicine, and art, 2025. 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. 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

6 authors.

Graham WildSchool of Science, University of New South Wales, Canberra, ACT 2612, Australia. g.wild@unsw.edu.au.ORCID http://orcid.org/0000-0003-1223-4675
Aziida NanyongaSchool of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, Australia.
Anam IqbalSchool of Science, University of New South Wales, Canberra, ACT 2612, Australia.
Shehar BanoSchool of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, Australia.
Alexander SomervilleSchool of Science, University of New South Wales, Canberra, ACT 2612, Australia.
Luke PollockSchool of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review examines the current applications, benefits, challenges, and future potential of artificial intelligence (AI) and immersive aviation technologies. AI has been applied across various domains, including flight operations, air traffic control, maintenance, and ground handling. AI enhances aviation safety by enabling pilot assistance systems, mitigating human error, streamlining safety management systems, and aiding in accident analysis. Lightweight AI models are crucial for mobile applications in aviation, particularly for resource-constrained environments such as drones. Hardware considerations involve trade-offs between energy-efficient field-programmable gate arrays and power-consuming graphics processing units. Battery and thermal management are critical for mobile device applications. Although AI integration has numerous benefits, including enhanced safety, improved efficiency, and reduced environmental impact, it also presents challenges. Addressing algorithmic bias, ensuring cybersecurity, and managing the relationship between human operators and AI systems are crucial. The future of aviation will likely involve even more sophisticated AI algorithms, advanced hardware, and increased integration of AI with augmented reality and virtual reality, creating new possibilities for training and operations, and ultimately leading to a safer, more efficient, and more sustainable aviation industry.

Indexed as

Artificial intelligenceAugmented realityAviationMobile computing

Identifiers

PMID40900374
PMCPMC12408884

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.