Evidence map›Paper›PMID 30079527›Full record

ReviewJournal of internal medicine2019

Smartphone-based clinical diagnostics: towards democratization of evidence-based health care.

I Hernández-Neuta, F Neumann, J Brightmeyer, T Ba Tis, N Madaboosi, Q Wei, A Ozcan, M Nilsson

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 1 pooled it
–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

76 citing papers in PubMed, 1 synthesis or guideline pooled it.

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16 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

I Hernández-NeutaDepartment of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, SE, Sweden.ORCID 0000-0001-7727-1712
F NeumannDepartment of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, SE, Sweden.
J BrightmeyerDepartment of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA.
T Ba TisDepartment of Materials Science and Engineering, North Carolina State University, Raleigh, NC, USA.
N MadaboosiDepartment of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, SE, Sweden.
Q WeiDepartment of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA.ORCID 0000-0003-1214-010X
A OzcanElectrical and Computer Engineering Department, University of California Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-0717-683X
M NilssonDepartment of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, SE, Sweden.ORCID 0000-0001-9985-0387

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advancements in bioanalytical techniques have led to the development of novel and robust diagnostic approaches that hold promise for providing optimal patient treatment, guiding prevention programs and widening the scope of personalized medicine. However, these advanced diagnostic techniques are still complex, expensive and limited to centralized healthcare facilities or research laboratories. This significantly hinders the use of evidence-based diagnostics for resource-limited settings and the primary care, thus creating a gap between healthcare providers and patients, leaving these populations without access to precision and quality medicine. Smartphone-based imaging and sensing platforms are emerging as promising alternatives for bridging this gap and decentralizing diagnostic tests offering practical features such as portability, cost-effectiveness and connectivity. Moreover, towards simplifying and automating bioanalytical techniques, biosensors and lab-on-a-chip technologies have become essential to interface and integrate these assays, bringing together the high precision and sensitivity of diagnostic techniques with the connectivity and computational power of smartphones. Here, we provide an overview of the emerging field of clinical smartphone diagnostics and its contributing technologies, as well as their wide range of areas of application, which span from haematology to digital pathology and rapid infectious disease diagnostics.

Indexed as

Evidence-Based PracticeSmartphoneBiosensing TechniquesCytological TechniquesHumansLab-On-A-Chip DevicesMicrofluidicsMobile ApplicationsPathology, MolecularPoint-of-Care SystemsPrinting, Three-DimensionalBiosensorsDiagnosticsDigital pathologyLab-on-a-chipSmartphones

Identifiers

PMID30079527
PMCPMC6334517

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.