{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T18:56:00Z","timestamp":1790967360200,"version":"4.1.0"},"reference-count":14,"publisher":"Society for Clinical Data Management","issue":"1","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,8]]},"abstract":"<jats:p>\u00a0 \u00a0 \u00a0 \u00a0 Generative AI is increasingly applied to design and manage quality management pyramid documents, supporting traceability and process integrity. As guidance from authorities such as the FDA, EMA, and WHO continues to evolve, organizations face significant challenges in establishing effective governance frameworks amid rapid technological advances. We review existing regional regulations and guidance, identifying core governance elements like transparency, data privacy, validation, and risk management. We propose a practical methodology for developing governance documentation, emphasizing the use of retrieval-augmented generation (RAG) techniques to safeguard confidentiality and improve accuracy in documentation. Additionally, we highlight the importance of integrating ethical principles, human in the loop (HITL), and lifecycle monitoring to promote responsible and compliant adoption of generative AI in clinical research. Our comprehensive approach aims to accelerate AI deployment while ensuring patient safety and regulatory compliance.<\/jats:p>","DOI":"10.47912\/jscdm.500","type":"journal-article","created":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T16:21:36Z","timestamp":1786206096000},"source":"Crossref","is-referenced-by-count":0,"title":["Generative AI and Clinical Development: Exploring the Frontier of AI Governance Utilizing Generative AI-Conceptual Framework for AI Governance in Clinical Development"],"prefix":"10.47912","volume":"0","author":[{"given":"Munenori","family":"Takata","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisuke","family":"Ichikawa","sequence":"additional","affiliation":[{"name":"Kowa Company Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaharu","family":"Harada","sequence":"additional","affiliation":[{"name":"ONO Pharmaceutical Co.,Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Makoto","family":"Hiraide","sequence":"additional","affiliation":[{"name":"Department of Clinical Assessment, Tokyo University of Pharmacy and Life Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mari","family":"Sugimoto","sequence":"additional","affiliation":[{"name":"A2 Healthcare Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukikazu","family":"Hayashi","sequence":"additional","affiliation":[{"name":"A2 Healthcare Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuhiro","family":"Yoshimaru","sequence":"additional","affiliation":[{"name":"Kowa Company Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Fujisawa","sequence":"additional","affiliation":[{"name":"Kowa Company Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiaki","family":"Nojima","sequence":"additional","affiliation":[{"name":"Kowa Company Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Fukimbara","sequence":"additional","affiliation":[{"name":"ONO Pharmaceutical Co.,Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideki","family":"Suganami","sequence":"additional","affiliation":[{"name":"Clinical Data Science, Kowa co., ltd."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7961-9417","authenticated-orcid":false,"given":"Takuhiro","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Biostatistics, Tohoku University Graduate School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27830","published-online":{"date-parts":[[2026,10,2]]},"reference":[{"key":"keyref_B1","unstructured":"1.\u00a0World Health Organization. Ethics and governance of artificial intelligence for health guidance on large multi-modal models. World Health Organization; 2024. Accessed February 10, 2026. https:\/\/iris.who.int\/server\/api\/core\/bitstreams\/e9e62c65-6045-481e-bd04-20e206bc5039\/content"},{"key":"keyref_B2","unstructured":"2.\u00a0US Food and Drug Administration. Considerations for the use of artificial intelligence to support regulatory decision-making for drug and biological products. Draft Guidance. US Department of Health and Human Services; 2025. Accessed February 10, 2026. https:\/\/www.fda.gov\/media\/184830\/download"},{"key":"keyref_B3","unstructured":"3.\u00a0European Medicines Agency. Reflection paper on the use of artificial intelligence (AI) in the medicinal product lifecycle. European Medicines Agency; 2024. Accessed February 10, 2026. https:\/\/www.ema.europa.eu\/en\/documents\/scientific-guideline\/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf"},{"issue":"2","key":"keyref_B4","doi-asserted-by":"publisher","first-page":"313","DOI":"10.22214\/ijraset.2021.32996","article-title":"Artificial intelligence at healthcare industry","volume":"9","author":"Gadde SSKalli VD","year":"2021","journal-title":"Int J Res Appl Sci Eng Technol"},{"key":"keyref_B5","unstructured":"5.\u00a0UK Medicines & Healthcare products Regulatory Agency. Impact of AI on the regulation of medical products. Implementing the AI white paper principles. HM Government; 2024. Accessed February 10, 2026. https:\/\/assets.publishing.service.gov.uk\/media\/662fce1e9e82181baa98a988\/MHRA_Impact-of-AI-on-the-regulation-of-medical-products.pdf"},{"key":"keyref_B6","unstructured":"6.\u00a0Canada\u2019s Drug Agency. Position statement on the use of artificial intelligence in the generation and reporting of evidence. Canada\u2019s Drug Agency; 2025. Accessed February 10, 2026. https:\/\/www.cda-amc.ca\/sites\/default\/files\/MG%20Methods\/Position_Statement_AI_Renumbered.pdf"},{"key":"keyref_B7","unstructured":"7.\u00a0Infocomm Media Development Authority of Singapore, Aicadium, AI Verify Foundation. Model AI governance framework for generative Al fostering a trusted ecosystem. IMDA; 2024. Accessed February 10, 2026. https:\/\/aiverifyfoundation.sg\/wp-content\/uploads\/2024\/05\/Model-AI-Governance-Framework-for-Generative-AI-May-2024-1-1.pdf"},{"key":"keyref_B8","unstructured":"8.\u00a0Cyberspace Administration of China, Office of the Central Cyberspace Affairs Commission. Interim measures for the management of generative artificial intelligence services. Published July 13, 2023. Accessed February 10, 2026. https:\/\/www.cac.gov.cn\/2023-07\/13\/c_1690898327029107.htm"},{"key":"keyref_B9","unstructured":"9.\u00a0Expert Group on how AI Principles Should be Implemented. AI governance in Japan ver. 1.1. Ministry of Economy, Trade and Industry; 2021. Accessed February 10, 2026. https:\/\/www.meti.go.jp\/shingikai\/mono_info_service\/ai_shakai_jisso\/pdf\/20210709_8.pdf"},{"issue":"1","key":"keyref_B10","doi-asserted-by":"publisher","DOI":"10.47912\/jscdm.486","article-title":"Governance for generative AI in clinical development: A cross-sectional survey in Japan","volume":"6","author":"Takata MSugimoto MHarada M","year":"2026","journal-title":"J Soc Clin Data Manage"},{"key":"keyref_B11","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume":"33","author":"Lewis PPerez EPiktus A","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"keyref_B12","doi-asserted-by":"publisher","DOI":"10.47912\/jscdm.448","article-title":"SCDM Executive Committee perspective: The future of clinical trials with AI and decentralized, hybrid and data-driven approaches","volume":"5","author":"Lemaire WNadolny PAndrus JSchaffer CCameron S","year":"2025","journal-title":"J Soc Clin Data Manage"},{"key":"keyref_B13","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2022.931439","article-title":"Governance of clinical AI applications to facilitate safe and equitable deployment in a large health system: key elements and early successes","volume":"4","author":"Liao FAdelaine SAfshar MPatterson BW","year":"2022","journal-title":"Front Digit Health"},{"key":"keyref_B14","unstructured":"14.\u00a0Society for Clinical Data Management. SCDM industry competency framework. Society for Clinical Data Management. Accessed February 10, 2026. https:\/\/scdm.org\/cdm-competency-framework\/"}],"container-title":["Journal of the Society for Clinical Data Management"],"original-title":[],"link":[{"URL":"https:\/\/www.jscdm.org\/article\/500\/galley\/349\/download\/","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T18:35:46Z","timestamp":1790966146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jscdm.org\/article\/id\/500\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,2]]},"references-count":14,"issue-title":["Digital First"],"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,10,23]]}},"URL":"https:\/\/doi.org\/10.47912\/jscdm.500","relation":{},"ISSN":["2694-1473"],"issn-type":[{"value":"2694-1473","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,2]]}}}