Evidence map›Paper›PMID 38920753›Full record

ArticleEpidemiologia (Basel, Switzerland)2024

AI-Enhanced Tools and Strategies for Airborne Disease Prevention in Cultural Heritage Sites.

Enrico Greco, Anastasia Serena Gaetano, Alessia De Spirt, Sabrina Semeraro, Prisco Piscitelli, Alessandro Miani, Saverio Mecca, Stela Karaj, Rita Trombin, Rachel Hodgton and 1 more

Abstract read
In one paragraph

Article in Epidemiologia (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. 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

11 authors.

Enrico GrecoDepartment of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgieri 1, 34127 Trieste, Italy.ORCID 0000-0003-1564-4661
Anastasia Serena GaetanoDepartment of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgieri 1, 34127 Trieste, Italy.ORCID 0000-0003-0071-7696
Alessia De SpirtDepartment of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgieri 1, 34127 Trieste, Italy.
Sabrina SemeraroDepartment of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgieri 1, 34127 Trieste, Italy.ORCID 0000-0002-3707-9117
Prisco PiscitelliItalian Society of Environmental Medicine (SIMA), Viale di Porta Vercellina, 9, 20123 Milan, Italy.ORCID 0000-0003-4556-6182
Alessandro MianiItalian Society of Environmental Medicine (SIMA), Viale di Porta Vercellina, 9, 20123 Milan, Italy.
Saverio MeccaDepartment of Architecture, University of Firenze, Via della Mattonaia 14, 50121 Firenze, Italy.
Stela KarajFaculty of Social Sciences, European University of Tirana, Rruga Xhanfize Keko, 1000 Tirana, Albania.ORCID 0000-0002-5472-1685
Rita TrombinItalian Academy of Biophilia (AIB), Lungadige Galtarossa 21, 37133 Verona, Italy.
Rachel HodgtonInternational WELL Building Institute, New York, NY 10001, USA.
Pierluigi BarbieriDepartment of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgieri 1, 34127 Trieste, Italy.ORCID 0000-0001-8847-6916

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the wake of the COVID-19 pandemic, the surveillance and safety measures of indoor Cultural Heritage sites have become a paramount concern due to the unique challenges posed by their enclosed environments and high visitor volumes. This communication explores the integration of Artificial Intelligence (AI) in enhancing epidemiological surveillance and health safety protocols in these culturally significant spaces. AI technologies, including machine learning algorithms and Internet of Things (IoT) sensors, have shown promising potential in monitoring air quality, detecting pathogens, and managing crowd dynamics to mitigate the spread of infectious diseases. We review various applications of AI that have been employed to address both direct health risks and indirect impacts such as visitor experience and preservation practices. Additionally, this paper discusses the challenges and limitations of AI deployment, such as ethical considerations, privacy issues, and financial constraints. By harnessing AI, Cultural Heritage sites can not only improve their resilience against future pandemics but also ensure the safety and well-being of visitors and staff, thus preserving these treasured sites for future generations. This exploration into AI's role in post-COVID surveillance at Cultural Heritage sites opens new frontiers in combining technology with traditional conservation and public health efforts, providing a blueprint for enhanced safety and operational efficiency in response to global health challenges.

Indexed as

Artificial IntelligenceCultural Heritage sitesin-door Environment

Identifiers

PMID38920753
PMCPMC11203220

What OpenQuestion holds

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LicenceCC BY
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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.