Evidence map›Paper›PMID 40529497›Full record

ReviewFrontiers in pharmacology2025

An artificial intelligence-aided scoping review of medicinal plant research in the Fertile Crescent.

Rawan Nihad Al-Sammarraie, Hadi Al Mubasher, Mariette Awad, Sally Naalbandian, Nadine Darwiche, Rami Zurayk, Maya Romani, Salma N Talhouk

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Trial
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

8 authors.

Rawan Nihad Al-SammarraieLandscape Design and Ecosystem Management, School of Architecture and Design, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
Hadi Al MubasherDepartment of Electrical and Computer Engineering, School of Architecture and Design, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
Mariette AwadDepartment of Electrical and Computer Engineering, School of Architecture and Design, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
Sally NaalbandianUniversity Libraries, American University of Beirut, Beirut, Lebanon.
Nadine DarwicheDepartment of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
Rami ZuraykLandscape Design and Ecosystem Management, School of Architecture and Design, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
Maya RomaniDepartment of Family Medicine, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
Salma N TalhoukLandscape Design and Ecosystem Management, School of Architecture and Design, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Arabic and Islamic Medicine (TAIM) originated in the seventh century, but unlike Chinese and Ayurvedic knowledge, TAIM has not evolved through evidence-based research and commercialization. Today, while global interest in traditional medicine is growing, TAIM ancestral knowledge remains unknown and unexplored. The purpose of this study is to provide baseline information on the status of TAIM research to guide future research and contribute to the growth of the sector. The focus of the study is the Fertile Crescent, a region of the Arab World endowed with a rich and diverse eco-geography. The method adopted was a scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The databases used included the Arab World Research Source: Al Masdar, CAB Direct, Iraqi Academic Scientific Journals, MEDLINE, Scopus, Web of Science and Google Scholar. The timeline of the search spanned from the database inception date to June 2024. The search led to 10,171 records which were subsequently reduced to 1,990 publications after deleting duplicates and performing a two-stage screening. Artificial intelligence (AI) technology was used to analyze the data focusing on reported plant species, treatment applications, study types and countries. The Generative Pretrained Transformer 4 (GPT-4) Turbo, a large language model, was used to extract the key features and the results were validated by the researchers. The findings revealed that the types of studies were mostly laboratory-based (86%), while few studies (14%) were field based. The top five treatment applications include cancer (29%), bacterial infections (22%), inflammation (12%), fungal infections (9%), and diabetes (8%). The most notable plant species that were under investigation in the various studies were

Indexed as

Arab medicineFertile Crescentherbal medicineIslamic medicinemedicinal plantspharmacology, artificial intelligence

Identifiers

PMID40529497
PMCPMC12171211

What OpenQuestion holds

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LicenceCC BY
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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.