Evidence map›Paper›PMID 42416918›Full record

ArticlePNAS nexus2026

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

Minkyo Lee, Xinyue Penny Pei, Si Hyung Jin, Natasha Shelby, Rani Gera, Alexander Viloria Winnett, Colin F Camerer, Mahbubur Rahman, Nils Pilotte, Steven A Williams and 1 more

Abstract read
In one paragraph

Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Minkyo LeeAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0000-0002-2714-179X
Xinyue Penny PeiDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0009-0003-9840-6243
Si Hyung JinDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
Natasha ShelbyDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0000-0001-9097-3663
Rani GeraDivision of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0000-0003-1888-4337
Alexander Viloria WinnettDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0000-0002-7338-5605
Colin F CamererDivision of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0000-0003-4049-1871
Mahbubur RahmanEnvironmental Health and WASH, Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh.ORCID https://orcid.org/0000-0003-0520-2683
Nils PilotteDepartment of Biological Sciences, Quinnipiac University, Hamden, CT 06518, USA.ORCID https://orcid.org/0000-0002-8447-7425
Steven A WilliamsDepartment of Biological Sciences, Smith College, Northampton, MA 01063, USA.ORCID https://orcid.org/0000-0002-4881-7496
Rustem F IsmagilovAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA.ORCID https://orcid.org/0000-0002-3680-4399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

diagnosticsglobal healthhuman learning and traininghuman–machine interactionresource-limited settings

Identifiers

PMID42416918
PMCPMC13339102

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.