ArticlePharmacoEconomics2026
A Machine Learning and Large Language Model Tool for Systematic Literature Reviews of Health Economic Evidence: A Validation Study.
Article in PharmacoEconomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystematic literature reviews offer high potential for efficiency gains from artificial intelligence (AI), now integrated into several systematic literature review software platforms. Validation studies show acceptable sensitivity, specificity, and accuracy for AI-assisted systematic literature reviews of clinical trial publications. Unlike trials, economic model publications lack consistency in content, terminology, and structure.
objectiveWe aimed to test the efficiency and accuracy of AI-assisted search, screening, and data extraction when applied to a systematic literature review of economic evaluations.
methodsA previously conducted manual systematic literature review of economic evaluations for chronic rhinosinusitis with nasal polyps was replicated using a machine learning-based inclusion prediction model (Robot Screener) and a large language model-based criteria screener (Smart Screener) within Nested Knowledge software, with performance benchmarked against the original human-conducted systematic literature review.
resultsThe AI-generated search retrieved 22/43 (51%) PubMed articles from the original systematic literature review. Accuracy exceeded 95% for title/abstract screening but fell below 80% for full-text screening. Extraction was reliable for high-level model descriptors and general study characteristics, but less so for model structures, health states, outcomes, and distinguishing sensitivity from scenario analyses and complex modeling assumptions for duration of response, discontinuation, surgery, and mortality. Estimated time savings ranged from ~20% (data extraction) to 60% (title/abstract screening and searches), varying by task human validation requirement.
conclusionsArtificial intelligence-driven tools performed well for title/abstract screening and general data extraction but were less accurate for full-text screening and interpretation of modeling choices. They can increase systematic literature review efficiency for economic evaluations but fall below the reliability seen for systematic literature reviews of clinical trials.
Identifiers
42525208What 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.