Evidence map›Paper›PMID 42661857›Full record

ArticleFrontiers in digital health2026

AI readiness for molecular precision medicine supply chains: evidence from Saudi Arabia.

Islam El-Nakib, Karim Soliman

Abstract read
In one paragraph

Article in Frontiers in digital health, 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

2 authors.

Islam El-NakibOperations and Supply Chain Management Department, College of Business, Effat University, Jeddah, Saudi Arabia.
Karim SolimanSupply Chain Management Department, College of Business Administration, University of Business and Technology, Jeddah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence (AI) is reshaping molecular precision medicine through biomarker discovery, genomic variant interpretation, molecular imaging, multi-omics analysis, and patient-specific therapeutic support. In Saudi Arabia, genomic medicine and digital health initiatives are expanding, yet limited evidence explains how AI readiness supports molecular diagnostics, genomic testing, biobanking, biological sample traceability, and targeted therapy delivery. Methods: A structured cross-sectional survey was completed by 350 professionals from hospitals, molecular diagnostic laboratories, genomic testing centers, biomedical laboratories, biobanks, pharmaceutical and biologic suppliers, and healthcare service providers in Saudi Arabia. Data were analyzed using partial least squares structural equation modeling. Results: AI readiness strengthened molecular data interoperability, biological sample traceability, diagnostic responsiveness, biomarker-guided decision support, and precision medicine service performance. Respondents from more AI-ready organizations reported stronger diagnostic responsiveness and fewer perceived workflow delays. Discussion: The findings connect AI readiness with molecular diagnostics, genomic services, biomarker analytics, biobanking, and healthcare supply chain coordination, providing evidence to support AI-enabled molecular medicine transformation in Saudi Arabia.

Indexed as

artificial intelligence readinessbiobankingbiological sample traceabilitybiomarker analysisgenomic testinghealthcare supply chainmolecular diagnosticsSaudi Arabia

Identifiers

PMID42661857
PMCPMC13518332

What OpenQuestion holds

Textmetadata
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.