Evidence map›Paper›PMID 40841498›Full record

ArticleTherapeutic innovation & regulatory science2026

Machine Learning in Tuberculosis Research: A Global Bibliometric Analysis of Diagnostic, Prognostic, and Drug Discovery Trends.

Siddig Ibrahim Abdelwahab, Manal Mohamed Elhassan Taha, Hazem Mathkour, Edrous Alamer, Saleh Mohammad Abdullah, Saeed Alshahrani, Abdullah Mohammed Farasani, Ahmed S Alamer, Jobran M Moshi, Khaled A Sahli and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in Therapeutic innovation & regulatory science, 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

13 authors.

Siddig Ibrahim AbdelwahabHealth Research Centre, Jazan University, Jazan, Saudi Arabia. sadiqa@jazanu.edu.sa.
Manal Mohamed Elhassan TahaDepartment of Applied Medical Laboratories, Faculty of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Hazem MathkourHealth Research Centre, Jazan University, Jazan, Saudi Arabia.
Edrous AlamerHealth Research Centre, Jazan University, Jazan, Saudi Arabia.
Saleh Mohammad AbdullahDepartment of Applied Medical Laboratories, Faculty of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Saeed AlshahraniDepartment of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan, Saudi Arabia.
Abdullah Mohammed FarasaniDepartment of Applied Medical Laboratories, Faculty of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Ahmed S AlamerDepartment of Health Education and Promotion, Faculty of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Jobran M MoshiDepartment of Applied Medical Laboratories, Faculty of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Khaled A SahliSecurity Forces Hospitals Program, General Directorate of Medical Services, Ministry of Interior, Riyadh, Saudi Arabia.
Mohammed JeraibyDepartment of Applied Medical Laboratories, Faculty of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Nizar A KhamjanDepartment of Applied Medical Laboratories, Faculty of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Abdulwahab BinjomahCollege of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia.

Funding

Jazan University 296
6 · The paper itself

Abstract

BACKGROUND AND

objectivesTuberculosis (TB) remains a major global health challenge, driving the need for innovative approaches in diagnosis and drug development. The integration of artificial intelligence (AI), particularly machine learning (ML), has enabled significant advancements in areas such as drug resistance prediction, radiomics, prognostic modeling, and computational drug discovery. This study presents a comprehensive bibliometric analysis of global research on machine learning and tuberculosis (MLTB), highlighting trends relevant to therapeutic innovation and regulatory science.

methodsA structured search of the Scopus database was conducted for English-language, data-driven publications on MLTB through May 1, 2024. Bibliometric indicators were analyzed using Biblioshiny and VOSviewer, focusing on publication trends, citation metrics, collaboration networks, and thematic clustering.

resultsThe MLTB research field has grown rapidly, with an average annual growth rate of 22.12% between 2000 and 2024. Publications averaged 21.64 citations, and 40.11% involved international collaboration. Twelve major clusters were identified, including deep learning, drug discovery, bioinformatics, docking, random forest, and latent TB infection-highlighting the field's expanding scope in drug development and diagnostic applications.

conclusionMLTB research is evolving rapidly, driven by interdisciplinary collaboration and AI innovation. These findings offer insights for guiding future AI-enabled TB therapeutic strategies and aligning research efforts with regulatory and translational priorities.

Indexed as

Antitubercular AgentsDrug DiscoveryMachine LearningTuberculosisBibliometricsHumansPrognosisAntitubercular AgentsBibliometric analysisDiagnostic applicationsGlobal collaborationMachine learningTuberculosis

Identifiers

PMID40841498

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.