Evidence map›Paper›PMID 42179680›Full record

ArticlePractical laboratory medicine2026

Development and validation of a rule-based tool for quality management reporting in a genetics laboratory.

Fouad Trad, Jana Doghman, Sarah Sayegh, Ali Chehab, Nada Assaf

Abstract read
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In one paragraph

Article in Practical laboratory medicine, 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

5 authors.

Fouad TradDepartment of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon.
Jana DoghmanDepartment of Pathology and Laboratory Medicine, American University of Beirut Medical Center, Lebanon.
Sarah SayeghDepartment of Pathology and Laboratory Medicine, American University of Beirut Medical Center, Lebanon.
Ali ChehabDepartment of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon.
Nada AssafDepartment of Pathology and Laboratory Medicine, American University of Beirut Medical Center, Lebanon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Quality management systems are essential in clinical laboratories to ensure optimal operational output. However, report generation still frequently relies on manual processes which are time-consuming and prone to errors. Methods: A rule-based artificial intelligence tool was internally developed to automate quality management report generation by directly extracting and processing electronic laboratory records from the health information system using pre-defined formulas and logic. Results: Implementation of this tool in a Medical Genetics laboratory reduced report preparation time by 90% and eliminated discrepancies compared to manual reports, alleviating the need for extensive secondary reviews. Conclusion: This AI-assisted approach streamlines quality management reporting, enhancing efficiency and data consistency. The successful development and implementation of those tools require continuous communication and validation between the different stakeholders for effective system refinement.

Indexed as

Artificial intelligence in laboratoriesCytogenetics quality indicatorsLaboratory automationQuality management systems

Identifiers

PMID42179680
PMCPMC13197744

What OpenQuestion holds

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Registered trials

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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.