SynthesisInternational journal of technology assessment in health care2026
A systematic literature review (SLR) on the adoption of artificial intelligence-assisted SLRS: implications for health technology assessments.
Synthesis in International journal of technology assessment in health care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- From Algorithm to Policy: A Bibliometric Analysis of Implementation Science and Governance Frameworks in AI Healthcare Research (2016-2026).Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectivesSystematic literature reviews (SLRs) are essential for evidence synthesis in healthcare decision making, including health technology assessment (HTA), but their time and resource demands are substantial. Artificial intelligence (AI) may enhance efficiency of conducting SLRs, but its acceptance by HTA bodies remains underexplored. This SLR quantifies published health-related SLRs reporting AI use, identifies AI tools used at each SLR stage, and evaluates HTA guidance on AI in evidence synthesis.
methodsWe searched Embase, Medline, and the Cochrane Library (up to 9 September 2025), supplemented by hand searches and reviews of HTA agency websites. Titles and abstracts were screened in Rayyan by a single reviewer, with full-text review confirming eligibility. Data were extracted and synthesized narratively along key themes.
resultsIn total, 112 studies covering 111 unique SLRs were identified, reporting 134 implementations of 45 unique AI tools (29 publicly available; 16 custom-built). AI use has risen since 2013 and was most frequently applied during title and abstract screening (88 of the 134 implementations). Human oversight remained essential, with no fully autonomous AI reported. Three HTA agencies (CDA-AMC, IQWiG, NICE), EUnetHTA, JBI and Cochrane have provided guidance, indicating the formal integration of AI into HTA processes.
conclusionsThis SLR provides a quantitative overview of AI use in health-related SLRs and current HTA guidance. These findings may inform development of clearer methodological recommendations and support integration of AI-assisted evidence synthesis in HTA submissions. Further research and policy development are needed to optimize its role in evidence synthesis and healthcare decision making.
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Identifiers
What OpenQuestion holds
Registered trials
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.