Evidence map›Paper›PMID 41553511›Full record

SynthesisEuropean journal of clinical pharmacology2026

Systematic review and meta-analysis of AI accuracy in warfarin dose prediction across ethnic groups.

Bassel Alrabadi, Mahmoud Marouf, Tareq Bashaireh, Alhumam Alqudah, Yamen Refai, Natalie Bandak

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in European journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Bassel AlrabadiFaculty of Medicine, Jordan University of Science and Technology, P.O Box 1730, Aqaba, 7710, Jordan. Bealrabadi22@med.just.edu.jo.
Mahmoud MaroufFaculty of Medicine, Jordan University of Science and Technology, P.O Box 1730, Aqaba, 7710, Jordan.
Tareq BashairehFaculty of Medicine, Jordan University of Science and Technology, P.O Box 1730, Aqaba, 7710, Jordan.
Alhumam AlqudahFaculty of Medicine, Jordan University of Science and Technology, P.O Box 1730, Aqaba, 7710, Jordan.
Yamen RefaiFaculty of Medicine, Jordan University of Science and Technology, P.O Box 1730, Aqaba, 7710, Jordan.
Natalie BandakFaculty of Medicine, Jordan University of Science and Technology, P.O Box 1730, Aqaba, 7710, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe primary purpose of this study is to systematically evaluate how accurately artificial intelligence (AI) models can predict optimal warfarin dosing by incorporating both genetic variations-particularly in VKORC1 and CYP2C9-and clinical parameters.

methodsWe searched PubMed, Scopus, and the Cochrane Library from inception until November 2024 for studies using machine learning to estimate warfarin dosing. Mean absolute error (MAE) was the primary outcome. Subgroup analyses were conducted by ethnicity. A random-effects model was used in R Software.

resultsSeventeen studies involving 50,859 patients were included. The pooled mean absolute error (MAE) using a random-effects model was 7.10 (95% CI: 5.52, 8.67; p < 0.001). Subgroup analysis by ethnicity revealed varying performance of AI-based warfarin dosing models. For the Asian population (4 studies), the pooled MAE was 4.45 (95% CI: 2.92, 5.97; p = 0.01). In contrast, the White population (3 studies) showed a higher pooled MAE of 10.25 (95% CI: 7.82, 12.68; p < 0.001), and the Black population (4 studies) had the highest pooled MAE at 12.27 (95% CI: 10.95, 13.59; p < 0.001). Funnel plot analysis revealed a symmetrical distribution of studies. For initial dosing algorithms, two studies reported MAEs of 0.24 and 5.43, respectively, reflecting variation based on population size and model type.

conclusionAI-based models enhance warfarin-dosing accuracy, but performance varies by ethnicity. Broader validation, especially in underrepresented groups such as Black populations, and methodological standardization are essential for equitable implementation.

Indexed as

AnticoagulantsArtificial IntelligenceWarfarinCytochrome P-450 CYP2C9EthnicityHumansVitamin K Epoxide ReductasesAnticoagulantsCYP2C9 protein, humanCytochrome P-450 CYP2C9Vitamin K Epoxide ReductasesVKORC1 protein, humanWarfarinArtificial intelligenceDosingMachine learningPharmacogeneticsPrecision medicineWarfarin

Identifiers

PMID41553511

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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.