Large Language Models in Clinical Trial Recruitment: Sociotechnical and Economic Framework Development Study — Qian (2026)
Abstract Background Large language models (LLMs) have shown substantial promise in patient-trial matching, but most published studies still evaluate the performance under controlled technical conditions rather than within real recruitment workflows. Less is known about how LLM-enabled clinical artificial intelligence (AI) systems should be embedded into organizational settings where privacy constraints, human oversight, patient-facing concerns, and governance costs shape deployment outcomes. Objective This study develops a theory-grounded conceptual framework for analyzing how LLM-enabled…
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