{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:51:04Z","timestamp":1792083064758,"version":"4.4.0"},"reference-count":164,"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>Part 1 of this series found that measured gains from artificial intelligence (AI) in health economics and outcomes research (HEOR) concentrate in tasks with observable ground truth and disappear where the quantity of interest is a counterfactual. What regulators, health technology assessment (HTA) bodies, and sponsors have said about that division has not been assembled in one place.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We assembled the primary regulatory, HTA, industry, and journal policy record through September 2026, requiring for each claim a named primary source and, where a quantity was reported, its provenance and whether the reporting party had a commercial interest. We downloaded the Food and Drug Administration (FDA) AI-enabled medical device list in full, tabulated published economic evaluations of AI, and set reported industry efficiency gains against disclosed use in submissions. We then evaluated the field against the task-based productivity framework of Nobel laureate Daron Acemoglu and derived a reporting standard from the documented failure modes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The FDA has authorized 1614 AI-enabled devices, 1230 (76.2%) in radiology, rising from 18 in 2016 to 335 in 2025, and has received more than 500 drug and biologic submissions containing AI components since 2016; its January 2025 credibility assessment guidance remains a draft and excludes discovery and operational uses. Industry sources report time savings of 40% to 97% in regulatory writing and approximately 80% in payer dossier drafting, but every one of these figures comes from a single-arm comparison without a concurrent control and was reported by the company that deployed or sold the tool. AI use was disclosed in 3 of 1391 HTA documents (0.2%) and 0.6% of one journal\u2019s submissions, against surveys reporting use between 25% and 81%. Nineteen economic evaluations of AI have been published against 1614 authorized devices. No regulator or HTA body has been documented accepting or rejecting an AI-derived endpoint as such.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Current disclosure instruments ask whether AI wrote the text. Reviewers need to know whether AI selected the evidence, defined the cohort, derived the endpoint, or generated the code, and whether the recommendation would change without it.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.36469\/001c.171942","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:02:16Z","timestamp":1792080136000},"update-policy":"https:\/\/doi.org\/10.36469\/00001","source":"Crossref","is-referenced-by-count":0,"title":["Faster but Not Truer, Part 2: The Regulatory Record, the Economics, and the TRACE-HEOR Reporting Standard"],"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 (CUNY)","department":["Graduate School of Public Health"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"22273","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"issue":"4","key":"ref1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s40273-021-01008-7","article-title":"Replication of published health economic obesity models: assessment of facilitators, hurdles and reproduction success","volume":"39","author":"B. Schwander","year":"2021","journal-title":"Pharmacoeconomics"},{"key":"ref2","article-title":"Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals: artificial intelligence use by authors","author":"International Committee of Medical Journal Editors"},{"key":"ref3","article-title":"Authorship and AI tools. COPE position statement","author":"Committee on Publication Ethics"},{"key":"ref4","article-title":"Editorial policies","author":"NEJM AI"},{"key":"ref5","article-title":"About NEJM AI","author":"NEJM AI"},{"key":"ref6","article-title":"Instructions for authors","author":"JAMA"},{"key":"ref7","article-title":"New article requirements","author":"Health Affairs"},{"key":"ref8","article-title":"Generative AI policies for journals","author":"Elsevier"},{"key":"ref9","article-title":"Artificial intelligence: editorial policies","author":"Springer Nature"},{"key":"ref10","article-title":"ISPOR artificial intelligence policy","author":"International Society for Pharmacoeconomics and Outcomes Research"},{"key":"ref11","article-title":"Artificial intelligence-enabled device software functions: lifecycle management and marketing submission recommendations. Draft guidance. Docket FDA-2024-D-4488","author":"US Food and Drug Administration","year":"2025"},{"key":"ref12","article-title":"Guide for Authors","author":"Value in Health"},{"key":"ref13","article-title":"Writing and reviewing for us in AI times","year":"2024","journal-title":"Lancet Infect Dis"},{"issue":"8063","key":"ref14","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1038\/d41586-025-01463-8","article-title":"Is it OK for AI to write science papers? Nature survey shows researchers are split","volume":"641","author":"D. Kwon","year":"2025","journal-title":"Nature"},{"issue":"8096","key":"ref15","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1038\/d41586-025-04066-5","article-title":"More than half of researchers now use AI for peer review, often against guidance","volume":"649","author":"M. Naddaf","year":"2025","journal-title":"Nature"},{"key":"ref16","article-title":"Augmented intelligence research: physician sentiments around the use of AI in health care","author":"American Medical Association","year":"2026"},{"key":"ref17","article-title":"AI usage among doctors doubles, confidence in technology grows","author":"American Medical Association","year":"2026"},{"key":"ref18","article-title":"Generative AI in healthcare: Adoption matures as agentic AI emerges","author":"McKinsey & Company","year":"2026"},{"key":"ref19","article-title":"AI Adoption by Health Systems Report 2024","author":"HIMSS"},{"key":"ref20","article-title":"AI in HEOR, RWD and medical affairs: what 133 professionals told us","author":"Polygon Health Analytics","year":"2026"},{"key":"ref21","article-title":"HEOR trends: results of a survey of HEOR leaders","author":"Center for the Evaluation of Value and Risk in Health, Tufts Medical Center","year":"2026"},{"key":"ref22","article-title":"Digital health advisory committee meeting: total product life cycle considerations for generative AI-enabled devices","author":"US Food and Drug Administration","year":"2024"},{"key":"ref23","article-title":"Workflows, not chatbots: findings from the 2026 AI in Medical Affairs Industry Benchmark Survey","author":"Medical Affairs Professional Society"},{"key":"ref24","article-title":"Medical affairs outlook report 2025","author":"ZS Associates"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"e91116","DOI":"10.2196\/91116","article-title":"A competency framework for medical AI education: mixed methods study","volume":"12","author":"C. Cai","year":"2026","journal-title":"JMIR Med Educ"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1584883","DOI":"10.3389\/fdgth.2025.1584883","article-title":"Perspective: advancing public health education by embedding AI literacy","volume":"7","author":"J. A. Acosta","year":"2025","journal-title":"Front Digit Health"},{"issue":"3","key":"ref27","doi-asserted-by":"publisher","first-page":"e72066","DOI":"10.1002\/hsr2.72066","article-title":"Artificial intelligence in public health education: a scoping review of workforce competency development","volume":"9","author":"M. M. A. Semi","year":"2026","journal-title":"Health Sci Rep"},{"issue":"4","key":"ref28","doi-asserted-by":"publisher","first-page":"430","DOI":"10.4300\/jgme-d-25-00009.1","article-title":"A framework for artificial intelligence in medical education: could I, would I, should I?","volume":"17","author":"R. P. F. Wolthusen","year":"2025","journal-title":"J Grad Med Educ"},{"issue":"4","key":"ref29","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1080\/0142159x.2024.2314198","article-title":"A scoping review of artificial intelligence in medical education: BEME Guide No. 84","volume":"46","author":"M. Gordon","year":"2024","journal-title":"Med Teach"},{"issue":"9","key":"ref30","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1016\/j.jval.2020.04.1834","article-title":"Competencies for professionals in health economics and outcomes research: the ISPOR Health Economics and Outcomes Research Competencies Framework","volume":"23","author":"L. T. Pizzi","year":"2020","journal-title":"Value Health"},{"key":"ref31","article-title":"2025 year-end digital health funding overview: a tale of two markets","author":"Rock Health"},{"key":"ref32","article-title":"2024 year-end market overview: Davids and Goliaths","author":"Rock Health"},{"key":"ref33","article-title":"FDA launches agency-wide AI tool to optimize performance for the American people","author":"US Food and Drug Administration","year":"2025"},{"key":"ref34","article-title":"Life sciences and AI report","author":"White & Case","year":"2026"},{"key":"ref35","article-title":"AI budgets grow in life sciences","author":"McKinsey & Company","year":"2025","journal-title":"Week in Charts"},{"key":"ref36","article-title":"Artificial intelligence in healthcare market size, share and trends analysis report","author":"Grand View Research"},{"key":"ref37","article-title":"The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2024: Daron Acemoglu, Simon Johnson and James A. Robinson, for studies of how institutions are formed and affect prosperity","author":"The Royal Swedish Academy of Sciences","year":"2024","journal-title":"NobelPrize.org"},{"issue":"121","key":"ref38","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1093\/epolic\/eiae042","article-title":"The simple macroeconomics of AI","volume":"40","author":"D. Acemoglu","year":"2024","journal-title":"Econ Policy"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3386\/w32487","article-title":"The simple macroeconomics of AI","author":"D. Acemoglu","year":"2024"},{"issue":"2","key":"ref40","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1257\/jep.33.2.3","article-title":"Automation and new tasks: how technology displaces and reinstates labor","volume":"33","author":"D. Acemoglu","year":"2019","journal-title":"J Econ Perspect"},{"issue":"6","key":"ref41","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1257\/aer.20160696","article-title":"The race between man and machine: implications of technology for growth, factor shares, and employment","volume":"108","author":"D. Acemoglu","year":"2018","journal-title":"Am Econ Rev"},{"issue":"6654","key":"ref42","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1126\/science.adh2586","article-title":"Experimental evidence on the productivity effects of generative artificial intelligence","volume":"381","author":"S. Noy","year":"2023","journal-title":"Science"},{"issue":"2","key":"ref43","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1093\/qje\/qjae044","article-title":"Generative AI at work","volume":"140","author":"E. Brynjolfsson","year":"2025","journal-title":"Q J Econ"},{"key":"ref44","article-title":"Reflection paper on the use of artificial intelligence in the lifecycle of medicines. EMA\/CHMP\/CVMP\/83833\/2023","author":"European Medicines Agency","year":"2024"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.3386\/w31161","article-title":"Generative AI at work","author":"E. Brynjolfsson","year":"2023"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2302.06590","article-title":"The impact of AI on developer productivity: evidence from GitHub Copilot","author":"S. Peng","year":"2023","journal-title":"arXiv"},{"issue":"2","key":"ref47","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1287\/orsc.2025.21838","article-title":"Navigating the jagged technological frontier: field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality","volume":"37","author":"F. Dell'Acqua","year":"2026","journal-title":"Organ Sci"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"G. S. Collins","year":"2024","journal-title":"BMJ"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"K. G. M. Moons","year":"2025","journal-title":"BMJ"},{"issue":"9","key":"ref50","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"26","author":"X. Liu","year":"2020","journal-title":"Nat Med"},{"issue":"9","key":"ref51","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"26","author":"R. S. Cruz","year":"2020","journal-title":"Nat Med"},{"issue":"5","key":"ref52","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"B. Vasey","year":"2022","journal-title":"Nat Med"},{"issue":"6","key":"ref53","doi-asserted-by":"publisher","first-page":"e047709","DOI":"10.1136\/bmjopen-2020-047709","article-title":"Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol","volume":"11","author":"V. Sounderajah","year":"2021","journal-title":"BMJ Open"},{"issue":"1","key":"ref54","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1038\/s41591-024-03425-5","article-title":"The TRIPOD-LLM reporting guideline for studies using large language models","volume":"31","author":"J. Gallifant","year":"2025","journal-title":"Nat Med"},{"key":"ref55","article-title":"Use of AI in evidence generation: NICE position statement","author":"National Institute for Health and Care Excellence","year":"2024","journal-title":"ECD11"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2307.08974","article-title":"Development of the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines","author":"G. E. Cacciamani","year":"2023","journal-title":"arXiv"},{"issue":"1","key":"ref57","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.jval.2021.11.1351","article-title":"Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Statement: updated reporting guidance for health economic evaluations","volume":"25","author":"D. Husereau","year":"2022","journal-title":"Value Health"},{"issue":"7","key":"ref58","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.jval.2022.03.022","article-title":"Machine learning methods in health economics and outcomes research: the PALISADE Checklist: a good practices report of an ISPOR Task Force","volume":"25","author":"W. V. Padula","year":"2022","journal-title":"Value Health"},{"issue":"11","key":"ref59","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. L. Fleurence","year":"2025","journal-title":"Value Health"},{"issue":"11","key":"ref60","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1016\/j.jval.2025.04.2167","article-title":"A taxonomy of generative artificial intelligence in health economics and outcomes research: an ISPOR Working Group Report","volume":"28","author":"R. L. Fleurence","year":"2025","journal-title":"Value Health"},{"issue":"2","key":"ref61","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.jval.2024.10.3846","article-title":"Generative artificial intelligence for health technology assessment: opportunities, challenges, and policy considerations: an ISPOR Working Group Report","volume":"28","author":"R. L. Fleurence","year":"2025","journal-title":"Value Health"},{"issue":"4","key":"ref62","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1007\/s12325-021-01659-0","article-title":"Characterization of a real-world response variable and comparison with RECIST-based response rates from clinical trials in advanced NSCLC","volume":"38","author":"X. Ma","year":"2021","journal-title":"Adv Ther"},{"issue":"1","key":"ref63","doi-asserted-by":"publisher","first-page":"e2500215","DOI":"10.1200\/cci-25-00215","article-title":"Ensuring reliability of curated electronic health record-derived data: the validation of accuracy for large language model-\/machine learning-extracted information and data (VALID) framework","volume":"10","author":"M. Estevez","year":"2026","journal-title":"JCO Clin Cancer Inform"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"e2300099","DOI":"10.1200\/cci.23.00099","article-title":"Validation of non-small cell lung cancer clinical insights using a generalized oncology natural language processing model","author":"R. C. Kenney","year":"2024","journal-title":"JCO Clin Cancer Inform"},{"key":"ref65","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1200\/cci.21.00065","article-title":"Automated extraction of tumor staging and diagnosis information from surgical pathology reports","author":"S. Abedian","year":"2021","journal-title":"JCO Clin Cancer Inform"},{"key":"ref66","article-title":"Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)","journal-title":"Official Journal of the European Union"},{"issue":"4","key":"ref67","doi-asserted-by":"publisher","first-page":"e70181","DOI":"10.1002\/trc2.70181","article-title":"Using AI-generated digital twins to boost clinical trial efficiency in Alzheimer's disease","volume":"11","author":"D. Wang","year":"2025","journal-title":"A&D Transl Res Clin Interv"},{"key":"ref68","doi-asserted-by":"publisher","first-page":"e55118","DOI":"10.2196\/55118","article-title":"Comparison of synthetic data generation techniques for control group survival data in oncology clinical trials: simulation study","volume":"12","author":"I. Akiya","year":"2024","journal-title":"JMIR Med Inform"},{"issue":"1","key":"ref69","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1038\/s41746-024-01359-3","article-title":"A scoping review of privacy and utility metrics in medical synthetic data","volume":"8","author":"B. Kaabachi","year":"2025","journal-title":"npj Digit Med"},{"issue":"3","key":"ref70","doi-asserted-by":"publisher","first-page":"e250128","DOI":"10.1001\/jamanetworkopen.2025.0128","article-title":"Implementing accuracy, completeness, and traceability for data reliability","volume":"8","author":"D. J. Riskin","year":"2025","journal-title":"JAMA Netw Open"},{"key":"ref71","doi-asserted-by":"publisher","first-page":"e64697","DOI":"10.2196\/64697","article-title":"A deep learning-enabled workflow to estimate real-world progression-free survival in patients with metastatic breast cancer: study using deidentified electronic health records","volume":"11","author":"G. Varma","year":"2025","journal-title":"JMIR Cancer"},{"key":"ref72","article-title":"EFPIA Position on the Use of Artificial Intelligence in the Medicinal Product Lifecycle","author":"European Federation of Pharmaceutical Industries and Associations","year":"2024"},{"key":"ref73","article-title":"IFPMA Artificial Intelligence Ethics Principles","author":"International Federation of Pharmaceutical Manufacturers and Associations","year":"2022"},{"key":"ref74","article-title":"Novartis commitment to the ethical and responsible use of AI systems","author":"Novartis"},{"key":"ref75","article-title":"Roche AI ethics principles","author":"Roche","year":"2025"},{"issue":"1","key":"ref76","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s41746-025-01457-w","article-title":"Language models for data extraction and risk of bias assessment in complementary medicine","volume":"8","author":"H. Lai","year":"2025","journal-title":"npj Digit Med"},{"key":"ref77","article-title":"Coverage Criteria and Utilization Management Requirements in CMS-4201-F","author":"Centers for Medicare and Medicaid Services","year":"2024","journal-title":"Health Plan Management System memorandum"},{"key":"ref78","article-title":"California Senate Bill 1120, Chapter 879, Statutes of 2024 (Physicians Make Decisions Act)"},{"key":"ref79","article-title":"Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models","author":"World Health Organization","year":"2024"},{"key":"ref80","article-title":"Ethics and Governance of Artificial Intelligence for Health: WHO Guidance","author":"World Health Organization","year":"2021"},{"key":"ref81","article-title":"Position statement on the use of artificial intelligence in the generation and reporting of evidence","author":"Canada's Drug Agency","year":"2025"},{"issue":"10","key":"ref82","doi-asserted-by":"publisher","DOI":"10.51731\/cjht.2024.1004","article-title":"Development of an evaluation instrument on artificial intelligence search tools for evidence synthesis","volume":"4","author":"CDA-AMC","year":"2024","journal-title":"CJHT"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.60584\/general-methods_v8.0","article-title":"General methods; version 8.0","author":"Institute for Quality and Efficiency in Health Care","year":"2026"},{"issue":"12","key":"ref84","doi-asserted-by":"publisher","first-page":"e230154","DOI":"10.57264\/cer-2023-0154","article-title":"Institute for Clinical and Economic Review: Peterson Health Technology Institute value assessment framework for digital health technologies","volume":"12","author":"S. D. Pearson","year":"2023","journal-title":"J Comp Eff Res"},{"key":"ref85","article-title":"Regulation (EU) 2021\/2282 of the European Parliament and of the Council of 15 December 2021 on health technology assessment","journal-title":"Official Journal of the European Union"},{"key":"ref86","article-title":"Implementation of the regulation on health technology assessment","author":"European Commission"},{"issue":"11","key":"ref87","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1016\/j.jval.2025.08.002","article-title":"A4SLR: an agentic artificial intelligence-assisted systematic literature review framework to augment evidence synthesis for health economics and outcomes research and health technology assessment","volume":"28","author":"K. Lee","year":"2025","journal-title":"Value Health"},{"key":"ref88","article-title":"Joint clinical assessments","author":"European Commission"},{"key":"ref89","article-title":"The HTA regulation. Presentation to the 15th EMA Industry Standing Group","author":"V. Barbuto","year":"2025"},{"key":"ref90","article-title":"Annual Work Programme 2026","author":"Health Technology Assessment Coordination Group","year":"2025"},{"key":"ref91","article-title":"Use of artificial intelligence and machine learning in evidence synthesis for health technology assessment submissions: a review of NICE, CDA, and IQWiG documents. Poster presented at: ISPOR 2026; Philadelphia, PA; May 17-20, 2026"},{"key":"ref92","article-title":"Use of artificial intelligence in health technology assessment submissions and agency practice"},{"key":"ref93","article-title":"Emerging trends in AI applications shaping pharmacoepidemiology and health technology assessments"},{"key":"ref94","article-title":"Assessing the use of artificial intelligence in publication-grade research: a literature review of applications in HEOR"},{"key":"ref95","article-title":"Artificial intelligence tools in health technology assessment: a scoping review","journal-title":"Conference proceeding OP07"},{"key":"ref96","article-title":"How AI is already reshaping cancer care","author":"Pfizer"},{"key":"ref97","article-title":"Merck expands innovative internal generative AI solutions, helping to deliver medicines to patients faster","author":"Merck & Co.","year":"2025"},{"issue":"2","key":"ref98","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s41669-024-00477-8","article-title":"Artificial intelligence to automate health economic modeling: a case study to evaluate the potential application of large language models","volume":"8","author":"T. Reason","year":"2024","journal-title":"Pharmacoeconomics Open"},{"key":"ref99","article-title":"Novo Nordisk transforms clinical documentation with Claude","author":"Anthropic","journal-title":"Customer story"},{"key":"ref100","article-title":"Aiming to enhance regulatory submissions with GenAI","author":"Takeda","year":"2024"},{"key":"ref101","article-title":"Automated generation of investigational new drug applications with large language models"},{"key":"ref102","article-title":"A tailored generative AI solution for regulatory writing","author":"Certara","year":"2024"},{"key":"ref103","article-title":"Drugmakers turn to AI to speed trials, regulatory submissions","author":"Reuters","year":"2026"},{"key":"ref104","article-title":"Merck and Google Cloud partner to accelerate agentic AI enterprise transformation","author":"Merck & Co.","year":"2026","journal-title":"News release"},{"key":"ref105","article-title":"Lilly partners with NVIDIA to build industry's most powerful AI supercomputer owned and operated by a pharmaceutical company","author":"Eli Lilly and Company","year":"2025"},{"key":"ref106","article-title":"Tempus signs expanded strategic agreements with AstraZeneca and Pathos AI","author":"Tempus AI","year":"2025"},{"key":"ref107","article-title":"Moderna: advancing mRNA medicine with generative AI. Customer story","author":"OpenAI"},{"key":"ref108","article-title":"Sanofi to become the first pharma company powered by artificial intelligence at scale","author":"Sanofi","year":"2023"},{"key":"ref109","article-title":"Replication package for artificial intelligence in health economics and outcomes research: a systematic scoping review","author":"O. Baser","year":"2026","journal-title":"GitHub repository"},{"issue":"S6","key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2026.03.1706","article-title":"Automated AMCP dossier development using a retrieval-augmented generation (RAG) based multi-agent approach. Abstract MSR207","volume":"29","year":"2026","journal-title":"Value Health"},{"key":"ref111","article-title":"AMCP format for formulary submissions","author":"Academy of Managed Care Pharmacy"},{"key":"ref112","article-title":"Norstella AI: principles and product capabilities","author":"Norstella"},{"key":"ref113","article-title":"Market access insights. Fact sheet","author":"IQVIA","year":"2023"},{"key":"ref114","article-title":"The first AI-designed drugs fall short in the clinic following years of hype","author":"Endpoints News"},{"key":"ref115","article-title":"Several months after Exscientia merger, AI outfit recursion reworks pipeline","author":"Fierce Biotech"},{"issue":"6","key":"ref116","doi-asserted-by":"publisher","first-page":"104009","DOI":"10.1016\/j.drudis.2024.104009","article-title":"How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons","volume":"29","author":"J. M. KP","year":"2024","journal-title":"Drug Discov Today"},{"issue":"2","key":"ref117","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1093\/biostatistics\/kxx069","article-title":"Estimation of clinical trial success rates and related parameters","volume":"20","author":"C. H. Wong","year":"2018","journal-title":"Biostatistics"},{"issue":"2","key":"ref118","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1093\/biostatistics\/kxy072","article-title":"Corrigendum: estimation of clinical trial success rates and related parameters","volume":"20","author":"C. H. Wong","year":"2018","journal-title":"Biostatistics"},{"issue":"4","key":"ref119","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1002\/cpt.3542","article-title":"Progress, pitfalls, and impact of AI-driven clinical trials","volume":"117","author":"D. Wilczok","year":"2024","journal-title":"Clin Pharmacol Ther"},{"key":"ref120","article-title":"Considerations for the use of artificial intelligence to support regulatory decision-making for drug and biological products. Draft guidance for industry. Docket FDA-2024-D-4689","author":"US Food and Drug Administration","year":"2025"},{"issue":"16_suppl","key":"ref121","doi-asserted-by":"publisher","first-page":"11072","DOI":"10.1200\/jco.2026.44.16_suppl.11072","article-title":"Clinical-stage AI-enabled therapeutic assets: an industry-wide analysis of scale, clinical progression, and company characteristics","volume":"44","author":"W. Haque","year":"2026","journal-title":"J Clin Oncol"},{"key":"ref122","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.jhealeco.2016.01.012","article-title":"Innovation in the pharmaceutical industry: new estimates of R&D costs","volume":"47","author":"J. A. DiMasi","year":"2016","journal-title":"J Health Econ"},{"issue":"9","key":"ref123","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1001\/jama.2020.1166","article-title":"Estimated research and development investment needed to bring a new medicine to market, 2009-2018","volume":"323","author":"O. J. Wouters","year":"2020","journal-title":"JAMA"},{"issue":"11","key":"ref124","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1001\/jama.2022.14317","article-title":"Errors in source data for study of drug development costs","volume":"328","author":"O. J. Wouters","year":"2022","journal-title":"JAMA"},{"issue":"11","key":"ref125","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1001\/jama.2022.14733","article-title":"Error in data in study of estimated cost of bringing a drug to market","volume":"328","year":"2022","journal-title":"JAMA"},{"issue":"6464","key":"ref126","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Z. Obermeyer","year":"2019","journal-title":"Science"},{"issue":"8","key":"ref127","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1001\/jamainternmed.2021.2626","article-title":"External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients","volume":"181","author":"A. Wong","year":"2021","journal-title":"JAMA Intern Med"},{"key":"ref128","doi-asserted-by":"publisher","first-page":"m1328","DOI":"10.1136\/bmj.m1328","article-title":"Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal","volume":"2020","author":"L. Wynants","journal-title":"BMJ"},{"key":"ref129","doi-asserted-by":"publisher","first-page":"n2281","DOI":"10.1136\/bmj.n2281","article-title":"Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review","author":"N. C. L. Andaur","year":"2021","journal-title":"BMJ"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1101\/2022.06.06.22276062","article-title":"Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction","author":"K. Rahmani","year":"2022"},{"key":"ref131","article-title":"FDA proposes framework to advance credibility of AI models used for drug and biological product submissions","author":"US Food and Drug Administration","year":"2025"},{"issue":"7","key":"ref132","doi-asserted-by":"publisher","first-page":"e0001534","DOI":"10.1371\/journal.pdig.0001534","article-title":"Validation is not enough: longitudinal evidence of post-deployment fragility in clinical AI systems","volume":"5","author":"G. Kopanitsa","year":"2026","journal-title":"PLOS Digit Health"},{"issue":"23","key":"ref133","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1001\/jama.2023.22295","article-title":"Measuring the impact of AI in the diagnosis of hospitalized patients","volume":"330","author":"S. Jabbour","year":"2023","journal-title":"JAMA"},{"issue":"4","key":"ref134","doi-asserted-by":"publisher","first-page":"e222176","DOI":"10.1148\/radiol.222176","article-title":"Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance","volume":"307","author":"T. Dratsch","year":"2023","journal-title":"Radiology"},{"issue":"10","key":"ref135","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1093\/jamia\/ocad118","article-title":"Artificial intelligence suppression as a strategy to mitigate artificial intelligence automation bias","volume":"30","author":"D. Wang","year":"2023","journal-title":"J Am Med Inform Assoc"},{"key":"ref136","doi-asserted-by":"publisher","first-page":"e58666","DOI":"10.2196\/58666","article-title":"Facilitating trust calibration in artificial intelligence-driven diagnostic decision support systems for determining physicians' diagnostic accuracy: quasi-experimental study","volume":"8","author":"T. Sakamoto","year":"2024","journal-title":"JMIR Form Res"},{"issue":"10","key":"ref137","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/s2468-1253(25)00133-5","article-title":"Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study","volume":"10","author":"K. Budzy\u0144","year":"2025","journal-title":"Lancet Gastroenterol Hepatol"},{"issue":"12","key":"ref138","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1016\/s2468-1253(25)00324-3","article-title":"Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: authors' reply","volume":"10","author":"K. Budzy\u0144","year":"2025","journal-title":"Lancet Gastroenterol Hepatol"},{"issue":"10","key":"ref139","doi-asserted-by":"publisher","first-page":"e2440969","DOI":"10.1001\/jamanetworkopen.2024.40969","article-title":"Large language model influence on diagnostic reasoning","volume":"7","author":"E. Goh","year":"2024","journal-title":"JAMA Netw Open"},{"key":"ref140","doi-asserted-by":"publisher","first-page":"e53164","DOI":"10.2196\/53164","article-title":"Hallucination rates and reference accuracy of ChatGPT and Bard for systematic reviews: comparative analysis","volume":"26","author":"M. Chelli","year":"2024","journal-title":"J Med Internet Res"},{"issue":"1","key":"ref141","doi-asserted-by":"publisher","first-page":"14045","DOI":"10.1038\/s41598-023-41032-5","article-title":"Fabrication and errors in the bibliographic citations generated by ChatGPT","volume":"13","author":"W. H. Walters","year":"2023","journal-title":"Sci Rep"},{"issue":"12","key":"ref142","doi-asserted-by":"publisher","DOI":"10.1056\/aipc2500801","article-title":"Leveraging artificial intelligence in drug and biological product development: an FDA and Clinical Trial Transformation Initiative Workshop Report","volume":"2","author":"A. Poddar","year":"2025","journal-title":"NEJM AI"},{"key":"ref143","article-title":"Trump officials downplay fake citations in high-profile report on children's health","author":"Science"},{"key":"ref144","article-title":"Anthropic expert accused of using AI-fabricated source in copyright case","author":"Reuters","year":"2025"},{"key":"ref145","article-title":"Federal court excludes expert for AI-hallucinated citations","author":"Washington State Bar Association","year":"2026","journal-title":"NWSidebar"},{"key":"ref146","article-title":"As Springer Nature journal clears AI papers, one university's retractions rise drastically","author":"Retraction Watch","year":"2025"},{"issue":"1","key":"ref147","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1038\/s41746-025-01722-y","article-title":"Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare","volume":"8","author":"A.R.A. El","year":"2025","journal-title":"npj Digit Med"},{"issue":"1","key":"ref148","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1186\/s12885-022-09613-1","article-title":"Cost-effectiveness of using artificial intelligence vs polygenic risk score to guide breast cancer screening","volume":"22","author":"S. Mital","year":"2022","journal-title":"BMC Cancer"},{"issue":"1","key":"ref149","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1038\/s41591-021-01599-w","article-title":"Optimizing risk-based breast cancer screening policies with reinforcement learning","volume":"28","author":"A. Yala","year":"2022","journal-title":"Nat Med"},{"issue":"1","key":"ref150","doi-asserted-by":"publisher","first-page":"6110","DOI":"10.1038\/s41467-023-41754-0","article-title":"Cost-effectiveness requirements for implementing artificial intelligence technology in the Women's UK Breast Cancer Screening service","volume":"14","author":"A. Vargas-Palacios","year":"2023","journal-title":"Nat Commun"},{"issue":"3","key":"ref151","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.jval.2021.08.015","article-title":"Assessing the economic value of clinical artificial intelligence: challenges and opportunities","volume":"25","author":"N. Hendrix","year":"2022","journal-title":"Value Health"},{"issue":"1","key":"ref152","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1186\/s12913-022-07655-6","article-title":"Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China","volume":"22","author":"X. Huang","year":"2022","journal-title":"BMC Health Serv Res"},{"key":"ref153","article-title":"PhRMA Comments on Docket FDA-2024-D-4689","author":"Pharmaceutical Research and Manufacturers of America"},{"key":"ref154","doi-asserted-by":"publisher","first-page":"S159","DOI":"10.1016\/j.ijtb.2025.11.019","article-title":"Reinforcement learning to optimize tuberculosis screening strategies in resource-limited settings","volume":"72","author":"P. N. Kalavadekar","year":"2025","journal-title":"Indian J Tuberc"},{"issue":"5","key":"ref155","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1007\/s43441-021-00302-y","article-title":"The use of external controls in FDA regulatory decision making","volume":"55","author":"M. Jahanshahi","year":"2021","journal-title":"Ther Innov Regul Sci"},{"issue":"1","key":"ref156","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1080\/10543406.2021.2011901","article-title":"Exploring the potential of external control arms created from patient-level data: a case study in non-small cell lung cancer","volume":"32","author":"X. Yin","year":"2022","journal-title":"J Biopharm Stat"},{"key":"ref157","article-title":"Qualification Opinion for Prognostic Covariate Adjustment (PROCOVA)","author":"European Medicines Agency","journal-title":"Committee for Medicinal Products for Human Use"},{"key":"ref158","doi-asserted-by":"publisher","first-page":"111208","DOI":"10.1016\/j.jclinepi.2023.10.024","article-title":"Transparency, openness, and reproducible research practices are frequently underused in health economic evaluations","volume":"165","author":"F. Catal\u00e1-L\u00f3pez","year":"2024","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"ref159","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1080\/17477778.2023.2260772","article-title":"Computer model and code sharing practices in healthcare discrete-event simulation: a systematic scoping review","volume":"19","author":"T. Monks","year":"2023","journal-title":"J Simul"},{"issue":"6","key":"ref160","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1016\/j.jval.2025.01.019","article-title":"Mapping the landscape of open-source health economic models: a systematic database review and analysis: an ISPOR Special Interest Group Report","volume":"28","author":"R. H. Henderson","year":"2025","journal-title":"Value Health"},{"issue":"10","key":"ref161","doi-asserted-by":"publisher","first-page":"e0275380","DOI":"10.1371\/journal.pone.0275380","article-title":"A meta-epidemiological assessment of transparency indicators of infectious disease models","volume":"17","author":"E. A. Zavalis","year":"2022","journal-title":"PLoS ONE"},{"key":"ref162","doi-asserted-by":"publisher","first-page":"k400","DOI":"10.1136\/bmj.k400","article-title":"Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in The BMJ and PLOS Medicine","volume":"2018","author":"F. Naudet","journal-title":"BMJ"},{"issue":"11","key":"ref163","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1007\/s40273-017-0553-x","article-title":"Replicating health economic models: firm foundations or a house of cards?","volume":"35","author":"I. Bermejo","year":"2017","journal-title":"Pharmacoeconomics"},{"key":"ref164","article-title":"Artificial intelligence\u2013enabled medical devices. Device list current as of September 4, 2026","author":"US Food and Drug Administration"}],"container-title":["Journal of Health Economics and Outcomes Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jheor.org\/article\/171942-faster-but-not-truer-part-2-the-regulatory-record-the-economics-and-the-trace-heor-reporting-standard","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:02:45Z","timestamp":1792080165000},"score":1,"resource":{"primary":{"URL":"https:\/\/jheor.org\/article\/171942-faster-but-not-truer-part-2-the-regulatory-record-the-economics-and-the-trace-heor-reporting-standard"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,15]]},"references-count":164,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.36469\/001c.171942","relation":{},"ISSN":["2327-2236"],"issn-type":[{"value":"2327-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,15]]}}}