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

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.

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