A train-and-assist device that upskills novices to strengthen the workforce and expand diagnostic access

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Authors: Minkyo Lee, Xinyue Pei, Si Hyung Jin, Natasha Shelby, Rani Gera, Alexander Viloria Winnett, Colin F. Camerer, Mahbubur M. Rahman, Nils Pilotte, Steven A. Williams, Rustem F. Ismagilov
Cited by: 0
References: 44

Abstract

Abstract AI and automation technologies are displacing millions of workers across industries in developed countries, while many developing nations continue to grapple with chronically high unemployment. Meanwhile, healthcare laboratories—particularly in resource-limited settings (including rural and community sites within high-income countries)—face acute shortages of trained staff and the high cost of molecular diagnostics. Here, we propose a “train-and-assist” class of devices that aims to both (i) upskill—rather than replace—workers and (ii) expand diagnostic capacity in a cost-effective way. We describe a device that trains and assists laboratory-inexperienced personnel to perform sample-pooling procedures, which enable high-performance molecular testing at lower costs and higher throughput. A 48-participant user study demonstrated that the device enabled both skill acquisition and high-accuracy pooling. A device-validation study using clinical stool specimens demonstrated that device-assisted pooling agreed 100% with individual assays for soil-transmitted helminths, which affect >1.5 billion people worldwide.

Fields of Study & Subjects

Biosensors and Analytical Detection Genetics Bioinformatics and Biomedical Research SARS-CoV-2 detection and testing

Publication details

Journal / source
PNAS Nexus
Volume
5
Issue
7
Pages
pgag211–pgag211
Publication date
29 June 2026
Language
EN