Evidence map›Paper›PMID 36967669›Full record

ReviewThe Journal of international medical research2023

Artificial intelligence in public health: the potential of epidemic early warning systems.

Chandini Raina MacIntyre, Xin Chen, Mohana Kunasekaran, Ashley Quigley, Samsung Lim, Haley Stone, Hye-Young Paik, Lina Yao, David Heslop, Wenzhao Wei and 2 more

Erratum issuedOpen access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The Journal of international medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 48 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 3 pooled it
22.3field-weighted citation impact, top 1% of its field
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

48 citing papers in PubMed, 3 syntheses or guidelines pooled it, 104 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 3 countries.

Chandini Raina MacIntyreBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Xin ChenBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.ORCID 0000-0002-9905-2307
Mohana KunasekaranBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Ashley QuigleyBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Samsung LimBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.ORCID 0000-0001-9838-8960
Haley StoneBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Hye-Young PaikSchool of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia.
Lina YaoSchool of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia.
David HeslopSchool of Population Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Wenzhao WeiBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Ines SarmientoBiosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Deepti GurdasaniWilliam Harvey Research Institute, Queen Mary University of London, United Kingdom.
UNSW Sydney · AUQueen Mary University of London · GB

Funding

Medical Research Council MR/S003711/1Medical Research Council MR/S003711/2
6 · The paper itself

Abstract

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Indexed as

BiosurveillanceEpidemicsArtificial IntelligenceHumansPublic HealthArtificial intelligencedigital surveillanceearly warning systemepidemic intelligencepandemicpublic health

Identifiers

PMID36967669
PMCPMC10052500
OpenAlexW4361010280

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.