{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T15:53:06Z","timestamp":1792079586823,"version":"4.4.0"},"reference-count":89,"publisher":"The Journal of Health Economics and Outcomes Research","issue":"2","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["jheor.org"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec id=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 (https:\/\/github.com\/obaser3791\/ai-heor-scoping-review) is publicly available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.36469\/001c.171937","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T15:37:16Z","timestamp":1792078636000},"update-policy":"https:\/\/doi.org\/10.36469\/00001","source":"Crossref","is-referenced-by-count":0,"title":["Faster but Not Truer, Part 1: A Systematic Scoping Review of Artificial Intelligence in Health Economics and Outcomes Research"],"prefix":"10.36469","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-5672","authenticated-orcid":false,"given":"Onur","family":"Baser","sequence":"first","affiliation":[{"name":"City University of New York, New York, USA","department":["Graduate School of Public Health"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"22273","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"issue":"11","key":"ref1","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1016\/j.jval.2025.06.018","article-title":"ELEVATE-GenAI: reporting guidelines for the use of large language models in health economics and outcomes research: an ISPOR Working Group Report","volume":"28","author":"R. 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