Faster but Not Truer, Part 1: A Systematic Scoping Review of Artificial Intelligence in Health Economics and Outcomes Research.

BACKGROUND: Artificial intelligence (AI) is being introduced into every stage of the health economics and outcomes research (HEOR) production line, from literature screening to causal estimation. The field lacks a defensible count of what has been done, and bibliometric claims about AI adoption circulate without measured error rates. METHODS: Following PRISMA-ScR, we searched PubMed for AI terms co-occurring with HEOR terms for publications from 2010 through 2025, retrieved 2642 records, and included 2476 after screening. Records were classified into 8 nonexclusive application domains and 1 of 6 method categories using a published term dictionary. To quantify classification error, an independent large language model classifier, blinded to the dictionary labels and given only title and abstract, reclassified a random sample of 300 included records. We report per label counts, agreement, Cohen kappa, prevalence-adjusted bias-adjusted kappa, precision, recall, F1, and a precision-adjusted corpus size with exact binomial intervals. We separately assembled the primary performance evidence for each HEOR workflow, requiring a quantitative metric from a primary study. RESULTS: Included publications rose from 29 in 2010 to 852 in 2025, a factor of 29.4, with the inflection at 2018 to 2019 rather than at the release of ChatGPT. AI reached 6.0% of all indexed HEOR output in 2025 while HEOR fell to 0.86% of all indexed medical AI output. Economic evaluation was the most frequently assigned domain (1559/2476, 63.0%), followed by real-world data and causal inference (496, 20.0%) and governance, reporting, and validity (494, 20.0%). Generative AI appeared in no record before 2021 and in 103 in 2025. Only 154 of 300 sampled records (51.3%; 95% confidence interval [CI], 45.7-56.9) were judged genuinely HEOR relevant, implying a precision-adjusted corpus of 1271 studies (95% CI 1131-1410). Agreement was substantial for real-world data and causal inference (kappa, 0.729) but negligible for economic evaluation (kappa, 0.144), where dictionary precision was 29.6%. Of 2476 records, 38 (1.5%) were randomized trials and 17 (0.7%) validation studies. The replication package is publicly available. CONCLUSIONS: The literature is growing faster than its own measurement infrastructure. Measured gains concentrate in tasks with observable ground truth, and predictive performance repeatedly fails to predict evidentiary performance.