Evidence map›Paper›PMID 41715226›Full record

ArticleSystematic reviews2026

Mapping the integration of artificial intelligence and digital technologies in health technology assessment: a scoping review protocol of global knowledge and practices.

Mohammed Alkhaldi, Rima Kachach, Malak Alrubaie, Wissam Ghach, Sara Al Dallal, Nuriya Musina, Shadi Albarqouni, Sulafa Ahmed, Dalia Dawoud, Abeer Al-Rabayah and 4 more

Abstract read
In one paragraph

Article in Systematic reviews, 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

14 authors.

Mohammed AlkhaldiPublic Health Department, School of Health Sciences and Psychology, Canadian University of Dubai, Dubai, UAE. mohammed.alkhaldi@cud.ac.ae.ORCID 0000-0001-5609-3806
Rima KachachPublic Health Department, School of Health Sciences and Psychology, Canadian University of Dubai, Dubai, UAE.
Malak AlrubaiePublic Health Department, School of Health Sciences and Psychology, Canadian University of Dubai, Dubai, UAE.
Wissam GhachPublic Health Department, School of Health Sciences and Psychology, Canadian University of Dubai, Dubai, UAE.
Sara Al DallalThe Emirates Health Economics Society, Dubai, UAE.
Nuriya MusinaThe Emirates Health Economics Society, Dubai, UAE.
Shadi AlbarqouniClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
Sulafa AhmedFaculty of Science and Engineering, School of Computing and Information Science, Anglia Ruskin University, Cambridge, UK.
Dalia DawoudEHTA Consulting, Cambridge, UK.
Abeer Al-RabayahCenter for Drug Policy and Technology Assessment, Department of Pharmacy, King Hussein Cancer Center, Amman, Jordan.
Mouna JameleddineHealth Technology Assessment Department, The National Authority for Assessment and Accreditation in Healthcare (INEAS), Tunis, Tunisia.
Ahmad Nader FasseehSyreon Middle East, Alexandria, Egypt.
Andrea QuaiattiniSchulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montréal, Canada.
Sara AhmedSchool of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, QC, Canada.

Funding

Dubai Future Foundation 2024CANAD-ALK-059Dubai Future Foundation RDI
6 · The paper itself

Abstract

backgroundHealth Technology Assessment (HTA) is a cornerstone of evidence for informing health policy and resource allocation globally. Rapid advancements and the proliferation of digital health technologies and artificial intelligence (AI) have prompted the re-examination of HTA processes and methods. While traditional approaches are manual and labor-intensive, HTA processes are now exploring the use of AI and other digital technologies for automation, decision support, and evidence synthesis. To date, however, there have been very limited studies that map the innovative technological solutions of HTA, the models of integration, and the associated barriers, facilitators, and governance considerations. As such, this scoping review aims to address this critical gap by mapping the landscape of the global knowledge and practices related to AI and DTs used in and for HTA and identifying the key barriers and enablers influencing their adoption, integration, and effective application within HTA processes.

methodsA scoping review will be conducted between August and November 2025, following the Arksey and O'Malley framework, enhanced by Joanna Briggs Institute (JBI) recommendations, and reported according to Preferred Reporting Items for Systematic Reviews and Meta‑Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Literature searches will be performed in electronic databases such as Medline (Ovid), Embase (Ovid), Global Health (Ovid), CINAHL (Ebsco), Scopus, Web of Science, and all regional indexes in the World Health Organization's Global Index Medicus, and other region-specific sources for studies published between 2020 and 2025. Eligible studies will include peer-reviewed articles and grey literature describing the integration of digitization, automation, and AI in global HTA processes. Dual independent screening, data extraction, and quality appraisal will be employed. DISCUSSION: Findings from this review will provide a map of how digitization, automation, and AI are integrated into HTA practice, highlighting key enablers, barriers, and knowledge gaps. The insights will be used to better guide researchers, policymakers, HTA agencies, and AI developers, further supporting future research and implementation strategies for better informed decision-making.

Indexed as

Artificial IntelligenceDigital TechnologyTechnology Assessment, BiomedicalDigital HealthHumansScoping Reviews as TopicArtificial IntelligenceDigital TechnologiesHealth Technology Assessment (HTA)Scoping Review Evidence

Identifiers

PMID41715226
PMCPMC13003739

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.