Evidence map›Paper›PMID 37498478›Full record

ReviewJournal of medical systems2023

Survey on Path Planning for UAVs in Healthcare Missions.

Ahmad Merei, Hamid Mcheick, Alia Ghaddar

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of medical systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ahmad Merei *Department of Computer Science and Mathematics, University of Quebec at Chicoutimi, 555 University Boulevard, Chicoutimi, G7H 2B1, Quebec, Canada. ahmad.merei1@uqac.ca.
Hamid Mcheick *Department of Computer Science and Mathematics, University of Quebec at Chicoutimi, 555 University Boulevard, Chicoutimi, G7H 2B1, Quebec, Canada.
Alia Ghaddar *Department of Computer Science, International University of Beirut, Mouseitbeh, Mazraa, PO Box: 146404, Beirut, Lebanon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents a comprehensive review of the state-of-the-art applications and methodologies related to the use of unmanned aerial vehicles (UAVs) in the healthcare sector, with a particular focus on path planning. UAVs have gained remarkable attention in healthcare during the outbreak of COVID-19, and this study explores their potential as a viable option for medical transportation. The survey categorizes existing studies by mission type, challenges addressed, and performance metrics to provide a clearer picture of the path planning problems and potential directions for future research. It highlights the importance of addressing the path planning problem and the challenges that UAVs may face during their missions, including the UAV delivery range limitation, and discusses recent solutions in this field. The study concludes by encouraging researchers to conduct their studies in a realistic environment to reveal UAVs' real potential, usability, and feasibility in the healthcare domain.

Indexed as

COVID-19BenchmarkingDisease OutbreaksHealth Care SectorHumansUnmanned Aerial DevicesCoverage path planningCOVID-19Delivery UAVHealth applicationsHealthcareHealth servicesInformative path planningUnmanned aerial vehicles

Identifiers

What OpenQuestion holds

Textmetadata
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.