- 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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