Large Language Models Across the Clinical Trial Lifecycle: A Systematic Review of Applications, Methodologies, and Validation Gaps

Large Language Models Across the Clinical Trial Lifecycle: A Systematic Review of Applications, Methodologies, and Validation Gaps | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 January 2026 V1 Latest version Share on Large Language Models Across the Clinical Trial Lifecycle: A Systematic Review of Applications, Methodologies, and Validation Gaps Authors : Huixue Zhou 0000-0002-6524-5506 [email protected] , Xinyu Zhou , Shuang Zhou , Meijia Song , Xiaoyi Chen , Yifan Wu , Yiyu Chen , … Show All … , Kai Yu , Yongkang Xiao , Yu Hou , Zaifu Zan , Xiangming Zhan , Shufan Ming , and Rui Zhang Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.176789264.41926181/v1 184 views 114 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: The integration of Large Language Models (LLMs) into clinical research promises to resolve systemic inefficiencies, yet the transition from theoretical utility to real-world deployment remains uneven. This systematic review analyzes the operationalization, technical optimization, and evidentiary maturity of LLMs across the clinical trial lifecycle. Methods: We searched seven major databases (including PubMed, IEEE Xplore, and Scopus) from January 2023 to August 2025, identifying 71 eligible studies. Studies were categorized according to an operational framework covering four trial stages from Design, Recruitment, Conduct, to Analysis. Data were synthesized regarding trial phase application, model architecture, governance mechanisms, and comparative performance against human experts. Results: Our analysis reveals a paradigm shift from passive data extraction to active operational roles. While analysis remains the dominant application (40.8%), significant expansion is observed in patient recruitment (31.0%) and trial design (18.3%), where models actively draft protocols and facilitate matching. A majority (77.6%) used open, secondary datasets, whereas only a minority (11 studies) operated on primary clinical data. Technically, the field is dominated by proprietary models (83.1%) and prompt engineering (69.0%), creating dependencies on opaque commercial architectures. Only 11 studies (14.1%) conducted 1 expert comparison, reflecting a persistent validation gap. Where evaluated, LLMs achieved clinician-comparable performance in eligibility screening but were less reliable in guideline-based reasoning. Conclusion: LLMs are beginning to function as operational aids in clinical research, particularly for trial drafting, screening, and evidence synthesis. However, real-world deployment is limited by reliance on simulated data, inconsistent privacy practices, and a persistent validation gap. Progress toward routine integration will require prospective evaluation, stronger governance frameworks, and multimodal capabilities that extend beyond text-only reasoning. Supplementary Material File (review_llm_for_clinical_trials_final.pdf) Download 2.84 MB Information & Authors Information Version history V1 Version 1 08 January 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License Keywords clinical trials generative ai large language models natural language processing systematic review Authors Affiliations Huixue Zhou 0000-0002-6524-5506 [email protected] University of Minnesota View all articles by this author Xinyu Zhou Northwestern University View all articles by this author Shuang Zhou View all articles by this author Meijia Song University of Minnesota View all articles by this author Xiaoyi Chen University of Minnesota View all articles by this author Yifan Wu University of Minnesota View all articles by this author Yiyu Chen University of Minnesota View all articles by this author Kai Yu University of Minnesota View all articles by this author Yongkang Xiao View all articles by this author Yu Hou University of Minnesota View all articles by this author Zaifu Zan University of Minnesota View all articles by this author Xiangming Zhan University of Minnesota Arizona State University View all articles by this author Shufan Ming University of Minnesota University of Illinois Urbana-Champaign View all articles by this author Rui Zhang University of Minnesota View all articles by this author Metrics & Citations Metrics Article Usage 184 views 114 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Huixue Zhou, Xinyu Zhou, Shuang Zhou, et al. Large Language Models Across the Clinical Trial Lifecycle: A Systematic Review of Applications, Methodologies, and Validation Gaps. Authorea . 08 January 2026. DOI: https://doi.org/10.22541/au.176789264.41926181/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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