Emerging point of care devices and artificial intelligence : prospects and challenges for public health

- Stranieri, Andrew, Venkatraman, Sitalakshmi, Minicz, John, Zarnegar, Armita, Firmin, Sally, Balasubramanian, Venki, Jelinek, Herbert

Title
Emerging point of care devices and artificial intelligence : prospects and challenges for public health
Creator
Stranieri, Andrew; Venkatraman, Sitalakshmi; Minicz, John; Zarnegar, Armita; Firmin, Sally; Balasubramanian, Venki; Jelinek, Herbert
Date
2022
Type
Text; Journal article
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/187947
Identifier
vital:17187
Identifier
https://doi.org/10.1016/j.smhl.2022.100279
Identifier
ISSN:2352-6483 (ISSN)
Abstract
Risk assessments for numerous conditions can now be performed cost-effectively and accurately using emerging point of care devices coupled with machine learning algorithms. In this article, the case is advanced that point of care testing in combination with risk assessments generated with artificial intelligence algorithms, applied to the universal screening of the general public for multiple conditions at one session, represents a new kind of in-expensive screening that can lead to the early detection of disease and other public health benefits. A case study of a diabetes screening clinic in a rural area of Australia is presented to illustrate its benefits. Universal, poly-aetiological screening is shown to meet the ten World Health Organisation criteria for screening programmes. © Elsevier Inc.
Publisher
Elsevier B.V.
Relation
Smart Health Vol. 24, no. (2022), p.
Rights
All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights
Copyright © 2022 Elsevier Inc.
Rights
Open Access
Subject
42 Health Sciences; 46 Information and computing sciences; 40 Engineering; Data analytics; mHealth; Point of care devices; Poly-aetiological screening; Risk assessment
Full Text
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